Overview

Brought to you by YData

Dataset statistics

Number of variables83
Number of observations15188
Missing cells129613
Missing cells (%)10.3%
Total size in memory9.6 MiB
Average record size in memory664.0 B

Variable types

Numeric45
Text37
Unsupported1

Alerts

neighbourhood_group_cleansed has constant value "18.0"Constant
calendar_updated has constant value "2022-07-31"Constant
reviews_per_month_imputed has constant value "0.0"Constant
days_since_last_review_imputed has constant value "0.0"Constant
description has 347 (2.3%) missing valuesMissing
neighborhood_overview has 7273 (47.9%) missing valuesMissing
host_location has 2568 (16.9%) missing valuesMissing
host_about has 5836 (38.4%) missing valuesMissing
host_response_time has 4321 (28.5%) missing valuesMissing
host_response_rate has 4320 (28.4%) missing valuesMissing
host_acceptance_rate has 3500 (23.0%) missing valuesMissing
host_is_superhost has 479 (3.2%) missing valuesMissing
host_neighbourhood has 1630 (10.7%) missing valuesMissing
neighbourhood has 7273 (47.9%) missing valuesMissing
neighbourhood_group_cleansed has 15187 (> 99.9%) missing valuesMissing
bathrooms has 4475 (29.5%) missing valuesMissing
bedrooms has 940 (6.2%) missing valuesMissing
beds has 4492 (29.6%) missing valuesMissing
price has 4480 (29.5%) missing valuesMissing
calendar_updated has 15187 (> 99.9%) missing valuesMissing
has_availability has 1332 (8.8%) missing valuesMissing
estimated_revenue_l365d has 4480 (29.5%) missing valuesMissing
first_review has 2912 (19.2%) missing valuesMissing
last_review has 2912 (19.2%) missing valuesMissing
review_scores_rating has 2912 (19.2%) missing valuesMissing
review_scores_accuracy has 2912 (19.2%) missing valuesMissing
review_scores_cleanliness has 2912 (19.2%) missing valuesMissing
review_scores_checkin has 2913 (19.2%) missing valuesMissing
review_scores_communication has 2913 (19.2%) missing valuesMissing
review_scores_location has 2915 (19.2%) missing valuesMissing
review_scores_value has 2915 (19.2%) missing valuesMissing
license has 15188 (100.0%) missing valuesMissing
neighbourhood_cleansed is highly skewed (γ1 = -123.037865)Skewed
longitude is highly skewed (γ1 = 123.1810562)Skewed
accommodates is highly skewed (γ1 = 34.27651483)Skewed
minimum_minimum_nights is highly skewed (γ1 = 20.34889274)Skewed
minimum_maximum_nights is highly skewed (γ1 = 41.04604532)Skewed
estimated_revenue_l365d is highly skewed (γ1 = 49.73683497)Skewed
review_scores_accuracy is highly skewed (γ1 = 105.3796147)Skewed
review_scores_checkin is highly skewed (γ1 = 65.92099942)Skewed
listing_url has unique valuesUnique
license is an unsupported type, check if it needs cleaning or further analysisUnsupported
bedrooms has 616 (4.1%) zerosZeros
availability_30 has 5683 (37.4%) zerosZeros
availability_60 has 4932 (32.5%) zerosZeros
availability_90 has 4479 (29.5%) zerosZeros
availability_365 has 3844 (25.3%) zerosZeros
number_of_reviews has 2911 (19.2%) zerosZeros
number_of_reviews_ltm has 6126 (40.3%) zerosZeros
number_of_reviews_l30d has 10623 (69.9%) zerosZeros
number_of_reviews_ly has 7095 (46.7%) zerosZeros
estimated_occupancy_l365d has 6126 (40.3%) zerosZeros
estimated_revenue_l365d has 2459 (16.2%) zerosZeros
calculated_host_listings_count_entire_homes has 2181 (14.4%) zerosZeros
calculated_host_listings_count_private_rooms has 11962 (78.8%) zerosZeros
calculated_host_listings_count_shared_rooms has 15039 (99.0%) zerosZeros
reviews_per_month has 2911 (19.2%) zerosZeros
reviews_per_month_imputed has 15186 (> 99.9%) zerosZeros
days_since_last_review_imputed has 15186 (> 99.9%) zerosZeros

Reproduction

Analysis started2025-10-22 11:57:20.170182
Analysis finished2025-10-22 11:57:24.212796
Duration4.04 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct15187
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.995245003 × 1017
Minimum5456
Maximum1.441660813 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:24.297167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5456
5-th percentile5050840.2
Q132154255.5
median6.973160057 × 1017
Q31.128969871 × 1018
95-th percentile1.388552453 × 1018
Maximum1.441660813 × 1018
Range1.441660813 × 1018
Interquartile range (IQR)1.128969871 × 1018

Descriptive statistics

Standard deviation5.520277695 × 1017
Coefficient of variation (CV)0.920775997
Kurtosis-1.660131816
Mean5.995245003 × 1017
Median Absolute Deviation (MAD)6.620542215 × 1017
Skewness0.05202444889
Sum9.104978586 × 1021
Variance3.047346583 × 1035
MonotonicityStrictly increasing
2025-10-22T11:57:24.406822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.441660813 × 10181
 
< 0.1%
54561
 
< 0.1%
57691
 
< 0.1%
64131
 
< 0.1%
1.4413756 × 10181
 
< 0.1%
1.44137271 × 10181
 
< 0.1%
1.441371622 × 10181
 
< 0.1%
1.441366131 × 10181
 
< 0.1%
1.441361193 × 10181
 
< 0.1%
1.441359294 × 10181
 
< 0.1%
Other values (15177)15177
99.9%
ValueCountFrequency (%)
54561
< 0.1%
57691
< 0.1%
64131
< 0.1%
64481
< 0.1%
85021
< 0.1%
ValueCountFrequency (%)
1.441660813 × 10181
< 0.1%
1.44165292 × 10181
< 0.1%
1.441636541 × 10181
< 0.1%
1.441587302 × 10181
< 0.1%
1.441460283 × 10181
< 0.1%

listing_url
Text

Unique 

Distinct15188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:24.582391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length48
Median length47
Mean length42.96174612
Min length14

Characters and Unicode

Total characters652503
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15188 ?
Unique (%)100.0%

Sample

1st rowhttps://www.airbnb.com/rooms/5456
2nd rowhttps://www.airbnb.com/rooms/5769
3rd rowhttps://www.airbnb.com/rooms/6413
4th rowhttps://www.airbnb.com/rooms/6448
5th rowhttps://www.airbnb.com/rooms/8502
ValueCountFrequency (%)
https://www.airbnb.com/rooms/85021
 
< 0.1%
https://www.airbnb.com/rooms/14416608131399873581
 
< 0.1%
https://www.airbnb.com/rooms/54561
 
< 0.1%
https://www.airbnb.com/rooms/57691
 
< 0.1%
https://www.airbnb.com/rooms/64131
 
< 0.1%
https://www.airbnb.com/rooms/14413756004958280451
 
< 0.1%
https://www.airbnb.com/rooms/14413797544528474861
 
< 0.1%
https://www.airbnb.com/rooms/14413882824955349571
 
< 0.1%
https://www.airbnb.com/rooms/14413921656763007761
 
< 0.1%
https://www.airbnb.com/rooms/14413979935135355191
 
< 0.1%
Other values (15180)15180
99.9%
2025-10-22T11:57:24.825884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/60748
 
9.3%
w45562
 
7.0%
o45562
 
7.0%
t30375
 
4.7%
r30375
 
4.7%
b30374
 
4.7%
s30374
 
4.7%
.30374
 
4.7%
m30374
 
4.7%
126717
 
4.1%
Other values (18)291668
44.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)652503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/60748
 
9.3%
w45562
 
7.0%
o45562
 
7.0%
t30375
 
4.7%
r30375
 
4.7%
b30374
 
4.7%
s30374
 
4.7%
.30374
 
4.7%
m30374
 
4.7%
126717
 
4.1%
Other values (18)291668
44.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)652503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/60748
 
9.3%
w45562
 
7.0%
o45562
 
7.0%
t30375
 
4.7%
r30375
 
4.7%
b30374
 
4.7%
s30374
 
4.7%
.30374
 
4.7%
m30374
 
4.7%
126717
 
4.1%
Other values (18)291668
44.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)652503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/60748
 
9.3%
w45562
 
7.0%
o45562
 
7.0%
t30375
 
4.7%
r30375
 
4.7%
b30374
 
4.7%
s30374
 
4.7%
.30374
 
4.7%
m30374
 
4.7%
126717
 
4.1%
Other values (18)291668
44.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:24.914649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.99934159
Min length4

Characters and Unicode

Total characters212622
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row20250613040113
2nd row20250613040113
3rd row20250613040113
4th row20250613040113
5th row20250613040113
ValueCountFrequency (%)
2025061304011315187
> 99.9%
1001
 
< 0.1%
2025-10-22T11:57:25.064465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
060750
28.6%
145562
21.4%
330374
14.3%
230374
14.3%
615187
 
7.1%
515187
 
7.1%
415187
 
7.1%
%1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)212622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
060750
28.6%
145562
21.4%
330374
14.3%
230374
14.3%
615187
 
7.1%
515187
 
7.1%
415187
 
7.1%
%1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)212622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
060750
28.6%
145562
21.4%
330374
14.3%
230374
14.3%
615187
 
7.1%
515187
 
7.1%
415187
 
7.1%
%1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)212622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
060750
28.6%
145562
21.4%
330374
14.3%
230374
14.3%
615187
 
7.1%
515187
 
7.1%
415187
 
7.1%
%1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:25.128915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.99953911
Min length3

Characters and Unicode

Total characters151873
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2025-06-13
2nd row2025-06-13
3rd row2025-06-14
4th row2025-06-13
5th row2025-06-13
ValueCountFrequency (%)
2025-06-139703
63.9%
2025-06-145484
36.1%
751
 
< 0.1%
2025-10-22T11:57:25.300033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
230374
20.0%
030374
20.0%
-30374
20.0%
515188
10.0%
615187
10.0%
115187
10.0%
39703
 
6.4%
45484
 
3.6%
71
 
< 0.1%
%1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)151873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
230374
20.0%
030374
20.0%
-30374
20.0%
515188
10.0%
615187
10.0%
115187
10.0%
39703
 
6.4%
45484
 
3.6%
71
 
< 0.1%
%1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)151873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
230374
20.0%
030374
20.0%
-30374
20.0%
515188
10.0%
615187
10.0%
115187
10.0%
39703
 
6.4%
45484
 
3.6%
71
 
< 0.1%
%1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)151873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
230374
20.0%
030374
20.0%
-30374
20.0%
515188
10.0%
615187
10.0%
115187
10.0%
39703
 
6.4%
45484
 
3.6%
71
 
< 0.1%
%1
 
< 0.1%

source
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:25.372710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length11
Mean length12.17632341
Min length1

Characters and Unicode

Total characters184934
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcity scrape
2nd rowcity scrape
3rd rowprevious scrape
4th rowcity scrape
5th rowcity scrape
ValueCountFrequency (%)
scrape15187
50.0%
city10718
35.3%
previous4469
 
14.7%
t1
 
< 0.1%
2025-10-22T11:57:25.519888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c25905
14.0%
e19656
10.6%
p19656
10.6%
r19656
10.6%
s19656
10.6%
a15187
8.2%
i15187
8.2%
15187
8.2%
t10719
5.8%
y10718
5.8%
Other values (3)13407
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)184934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c25905
14.0%
e19656
10.6%
p19656
10.6%
r19656
10.6%
s19656
10.6%
a15187
8.2%
i15187
8.2%
15187
8.2%
t10719
5.8%
y10718
5.8%
Other values (3)13407
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)184934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c25905
14.0%
e19656
10.6%
p19656
10.6%
r19656
10.6%
s19656
10.6%
a15187
8.2%
i15187
8.2%
15187
8.2%
t10719
5.8%
y10718
5.8%
Other values (3)13407
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)184934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c25905
14.0%
e19656
10.6%
p19656
10.6%
r19656
10.6%
s19656
10.6%
a15187
8.2%
i15187
8.2%
15187
8.2%
t10719
5.8%
y10718
5.8%
Other values (3)13407
7.2%

name
Text

Distinct14827
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:25.723926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length106
Median length70
Mean length38.13089281
Min length1

Characters and Unicode

Total characters579132
Distinct characters187
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14648 ?
Unique (%)96.4%

Sample

1st rowWalk to 6th, Rainey St and Convention Ctr
2nd rowNW Austin Room
3rd rowGem of a Studio near Downtown
4th rowSecluded Studio @ Zilker - King Bed, Bright & Airy
5th rowWoodland Studio Lodging
ValueCountFrequency (%)
6276
 
6.3%
austin4276
 
4.3%
in2534
 
2.5%
downtown2090
 
2.1%
home1972
 
2.0%
to1960
 
2.0%
pool1508
 
1.5%
private1250
 
1.3%
modern1228
 
1.2%
the1090
 
1.1%
Other values (6820)75278
75.7%
2025-10-22T11:57:26.060290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84617
 
14.6%
o42357
 
7.3%
e40983
 
7.1%
t36286
 
6.3%
n31677
 
5.5%
i29886
 
5.2%
a29466
 
5.1%
r25010
 
4.3%
s21050
 
3.6%
u17848
 
3.1%
Other values (177)219952
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)579132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
84617
 
14.6%
o42357
 
7.3%
e40983
 
7.1%
t36286
 
6.3%
n31677
 
5.5%
i29886
 
5.2%
a29466
 
5.1%
r25010
 
4.3%
s21050
 
3.6%
u17848
 
3.1%
Other values (177)219952
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)579132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
84617
 
14.6%
o42357
 
7.3%
e40983
 
7.1%
t36286
 
6.3%
n31677
 
5.5%
i29886
 
5.2%
a29466
 
5.1%
r25010
 
4.3%
s21050
 
3.6%
u17848
 
3.1%
Other values (177)219952
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)579132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
84617
 
14.6%
o42357
 
7.3%
e40983
 
7.1%
t36286
 
6.3%
n31677
 
5.5%
i29886
 
5.2%
a29466
 
5.1%
r25010
 
4.3%
s21050
 
3.6%
u17848
 
3.1%
Other values (177)219952
38.0%

description
Text

Missing 

Distinct13246
Distinct (%)89.3%
Missing347
Missing (%)2.3%
Memory size118.8 KiB
2025-10-22T11:57:26.294765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1000
Median length526
Mean length393.3433731
Min length1

Characters and Unicode

Total characters5837609
Distinct characters459
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12777 ?
Unique (%)86.1%

Sample

1st rowGreat central location for walking to Convention Center, Rainey Street, East 6th Street, Downtown, Congress Ave Bats.<br /><br /> Free wifi<br /><br />No Smoking, No pets
2nd rowGreat studio apartment, perfect a single person or a couple. Available as a month-to-month rental. If you're looking for a different month than the one that's open, please ask. Just 1 mile into downtown. Convenient for walking, biking, rideshare or busing into downtown, UT campus and other central Austin spots. Walk to the 10-mile looped Town Lake Trail. Airy space with very nice amenities, fresh coffee beans and a private patio.
3rd rowClean, private space with everything you need for a quiet, comfy, private stay close to Zilker Park and Barton Springs, the river, parks, trails, and downtown. King bed, vaulted ceilings, high-speed fiber internet. Quality furnishings and amenities will make you feel at home. We offer contactless check-in/checkout, if you like (and we are vaccinated).
4th rowStudio rental on lower level of home located in a 1950s neighborhood less than two miles from downtown Austin and close to bus routes.<br /><br />On stays less than 30 nights additional Austin city hotel taxes of 11% will be collected separately following confirmation of reservation.<br /><br />Texas state hotel taxes will be collected by Airbnb.<br /><br />Hotel taxes apply for all stays of 29 nights or less. No hotel taxes are charged for rentals of 30 nights or more.
5th rowComfortable 2 bedroom/2 bathroom home very centrally located on Austin’s East Side. Walking distance to the Convention Center, Whole Foods, Target, CVS, plus endless local establishments and attractions.<br /><br />Sorry, no pets.
ValueCountFrequency (%)
and38222
 
4.0%
the35596
 
3.7%
a28163
 
2.9%
to25629
 
2.7%
in18155
 
1.9%
of15801
 
1.6%
br14322
 
1.5%
with13555
 
1.4%
austin13409
 
1.4%
for12911
 
1.3%
Other values (23064)749892
77.7%
2025-10-22T11:57:26.654301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
958965
16.4%
e481817
 
8.3%
t375414
 
6.4%
o369504
 
6.3%
a342528
 
5.9%
r313526
 
5.4%
i311162
 
5.3%
n310678
 
5.3%
s274538
 
4.7%
l183123
 
3.1%
Other values (449)1916354
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5837609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
958965
16.4%
e481817
 
8.3%
t375414
 
6.4%
o369504
 
6.3%
a342528
 
5.9%
r313526
 
5.4%
i311162
 
5.3%
n310678
 
5.3%
s274538
 
4.7%
l183123
 
3.1%
Other values (449)1916354
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5837609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
958965
16.4%
e481817
 
8.3%
t375414
 
6.4%
o369504
 
6.3%
a342528
 
5.9%
r313526
 
5.4%
i311162
 
5.3%
n310678
 
5.3%
s274538
 
4.7%
l183123
 
3.1%
Other values (449)1916354
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5837609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
958965
16.4%
e481817
 
8.3%
t375414
 
6.4%
o369504
 
6.3%
a342528
 
5.9%
r313526
 
5.4%
i311162
 
5.3%
n310678
 
5.3%
s274538
 
4.7%
l183123
 
3.1%
Other values (449)1916354
32.8%

neighborhood_overview
Text

Missing 

Distinct6332
Distinct (%)80.0%
Missing7273
Missing (%)47.9%
Memory size118.8 KiB
2025-10-22T11:57:26.848869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1000
Median length762
Mean length399.2403032
Min length1

Characters and Unicode

Total characters3159987
Distinct characters215
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5823 ?
Unique (%)73.6%

Sample

1st rowMy neighborhood is ideally located if you want to walk to bars and restaurants downtown, East 6th Street or Rainey Street. The Convention Center is only 3 1/2 blocks away and a quick 10 minute walk. Whole foods store located 5 blks , easily walkable.
2nd rowQuiet neighborhood with lots of trees and good neighbors.
3rd rowTravis Heights is one of the oldest neighborhoods in Austin. Our house was built in 1937. We rebuilt the apartment in 2009 (well, finished and furnished it for rental then). From the studio it's a pretty easy 1-mile walk through the neighborhood to all the shops and restaurants on South Congress.
4th rowThe neighborhood is fun and funky (but quiet)! People are friendly and you can't beat the location.
5th rowEast Cesar Chavez is a gentrifying urban area that remains highly diverse. Guests can walk to downtown, the Convention Center, Lady Bird Lake, Rainey Street, E. 6th Street, Franklin Barbecue, taco trailers, coffee shops, hot spots, and hidden gems.
ValueCountFrequency (%)
the22329
 
4.3%
and21523
 
4.2%
a13524
 
2.6%
to12446
 
2.4%
of12030
 
2.3%
is9586
 
1.9%
br8045
 
1.6%
austin7885
 
1.5%
in7473
 
1.4%
5995
 
1.2%
Other values (14747)396939
76.7%
2025-10-22T11:57:27.145222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
513988
16.3%
e255348
 
8.1%
t200390
 
6.3%
a192652
 
6.1%
o188548
 
6.0%
i182144
 
5.8%
r173141
 
5.5%
n172466
 
5.5%
s164249
 
5.2%
l100384
 
3.2%
Other values (205)1016677
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3159987
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
513988
16.3%
e255348
 
8.1%
t200390
 
6.3%
a192652
 
6.1%
o188548
 
6.0%
i182144
 
5.8%
r173141
 
5.5%
n172466
 
5.5%
s164249
 
5.2%
l100384
 
3.2%
Other values (205)1016677
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3159987
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
513988
16.3%
e255348
 
8.1%
t200390
 
6.3%
a192652
 
6.1%
o188548
 
6.0%
i182144
 
5.8%
r173141
 
5.5%
n172466
 
5.5%
s164249
 
5.2%
l100384
 
3.2%
Other values (205)1016677
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3159987
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
513988
16.3%
e255348
 
8.1%
t200390
 
6.3%
a192652
 
6.1%
o188548
 
6.0%
i182144
 
5.8%
r173141
 
5.5%
n172466
 
5.5%
s164249
 
5.2%
l100384
 
3.2%
Other values (205)1016677
32.2%
Distinct14751
Distinct (%)97.1%
Missing1
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:27.317289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length156
Median length152
Mean length104.2608152
Min length3

Characters and Unicode

Total characters1583409
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14539 ?
Unique (%)95.7%

Sample

1st rowhttps://a0.muscache.com/pictures/14084884/b5a35a84_original.jpg
2nd rowhttps://a0.muscache.com/pictures/23822033/ac946aff_original.jpg
3rd rowhttps://a0.muscache.com/pictures/hosting/Hosting-U3RheVN1cHBseUxpc3Rpbmc6NjQxMw%3D%3D/original/924415d8-11e4-4404-8394-47b713a6c7ba.jpeg
4th rowhttps://a0.muscache.com/pictures/airflow/Hosting-6448/original/a0ab6e9c-58ed-4d57-acb1-60cb68b068e0.jpg
5th rowhttps://a0.muscache.com/pictures/miso/Hosting-8502/original/be48ea3b-6d8a-4d9b-bc13-8aa514ef246a.jpeg
ValueCountFrequency (%)
https://a0.muscache.com/pictures/miso/hosting-1400747442355622112/original/9334941f-4948-4c32-8e54-660e509984dd.png30
 
0.2%
https://a0.muscache.com/pictures/miso/hosting-1436371971184914139/original/cb7e6581-0876-4e35-bc2b-94e1cc725c4d.png27
 
0.2%
https://a0.muscache.com/pictures/miso/hosting-988608947748806022/original/3e149274-6c0b-4437-82ec-00dd852f5394.jpeg26
 
0.2%
https://a0.muscache.com/pictures/935d48b1-3da3-4e3b-bfa0-3270ee8a37ab.jpg12
 
0.1%
https://a0.muscache.com/pictures/miso/hosting-1270676228136673992/original/c9df3267-f285-4d86-8ef5-bb23dad7e4c7.jpeg12
 
0.1%
https://a0.muscache.com/pictures/miso/hosting-963096894228517317/original/c27ef598-627b-433f-8e80-dde3c45d08b9.jpeg11
 
0.1%
https://a0.muscache.com/pictures/miso/hosting-1284004198019982821/original/237a3f25-f10b-408c-85d4-18aeb73e0667.png10
 
0.1%
https://a0.muscache.com/pictures/miso/hosting-1285476474707857329/original/073ef7a8-3d03-45ac-b8df-df7a382a386b.png10
 
0.1%
https://a0.muscache.com/pictures/miso/hosting-1285616314916789543/original/246eef5d-19f0-4990-b9ce-3cc11ff13765.png10
 
0.1%
https://a0.muscache.com/pictures/14460691/c93eba3e_original.jpg8
 
0.1%
Other values (14741)15031
99.0%
2025-10-22T11:57:27.591417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c92979
 
5.9%
/92252
 
5.8%
a75209
 
4.7%
-70477
 
4.5%
e70110
 
4.4%
s67144
 
4.2%
t60939
 
3.8%
057905
 
3.7%
i57627
 
3.6%
457298
 
3.6%
Other values (52)881469
55.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1583409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c92979
 
5.9%
/92252
 
5.8%
a75209
 
4.7%
-70477
 
4.5%
e70110
 
4.4%
s67144
 
4.2%
t60939
 
3.8%
057905
 
3.7%
i57627
 
3.6%
457298
 
3.6%
Other values (52)881469
55.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1583409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c92979
 
5.9%
/92252
 
5.8%
a75209
 
4.7%
-70477
 
4.5%
e70110
 
4.4%
s67144
 
4.2%
t60939
 
3.8%
057905
 
3.7%
i57627
 
3.6%
457298
 
3.6%
Other values (52)881469
55.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1583409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c92979
 
5.9%
/92252
 
5.8%
a75209
 
4.7%
-70477
 
4.5%
e70110
 
4.4%
s67144
 
4.2%
t60939
 
3.8%
057905
 
3.7%
i57627
 
3.6%
457298
 
3.6%
Other values (52)881469
55.7%

host_id
Real number (ℝ)

Distinct8878
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181778789.5
Minimum4
Maximum700650145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:27.669441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile1742202.65
Q118491201
median84767145
Q3332671291.2
95-th percentile574279333
Maximum700650145
Range700650141
Interquartile range (IQR)314180090.2

Descriptive statistics

Standard deviation200861862.7
Coefficient of variation (CV)1.104979648
Kurtosis-0.4463612138
Mean181778789.5
Median Absolute Deviation (MAD)79125188.5
Skewness0.9602349721
Sum2.760856254 × 1012
Variance4.034548789 × 1016
MonotonicityNot monotonic
2025-10-22T11:57:27.772645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107434423116
 
0.8%
1825322697
 
0.6%
816744794
 
0.6%
50199927890
 
0.6%
50199951483
 
0.5%
46440491973
 
0.5%
27455233365
 
0.4%
3581756160
 
0.4%
45647591459
 
0.4%
44760616957
 
0.4%
Other values (8868)14394
94.8%
ValueCountFrequency (%)
41
 
< 0.1%
233
< 0.1%
7961
 
< 0.1%
51462
< 0.1%
80281
 
< 0.1%
ValueCountFrequency (%)
7006501451
< 0.1%
6998180951
< 0.1%
6996454401
< 0.1%
6993378801
< 0.1%
6979279931
< 0.1%
Distinct8878
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:27.951166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length42
Mean length42.26099552
Min length18

Characters and Unicode

Total characters641860
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7032 ?
Unique (%)46.3%

Sample

1st rowhttps://www.airbnb.com/users/show/8028
2nd rowhttps://www.airbnb.com/users/show/8186
3rd rowhttps://www.airbnb.com/users/show/13879
4th rowhttps://www.airbnb.com/users/show/14156
5th rowhttps://www.airbnb.com/users/show/25298
ValueCountFrequency (%)
https://www.airbnb.com/users/show/107434423116
 
0.8%
https://www.airbnb.com/users/show/1825322697
 
0.6%
https://www.airbnb.com/users/show/816744794
 
0.6%
https://www.airbnb.com/users/show/50199927890
 
0.6%
https://www.airbnb.com/users/show/50199951483
 
0.5%
https://www.airbnb.com/users/show/46440491973
 
0.5%
https://www.airbnb.com/users/show/27455233365
 
0.4%
https://www.airbnb.com/users/show/3581756160
 
0.4%
https://www.airbnb.com/users/show/45647591459
 
0.4%
https://www.airbnb.com/users/show/44760616957
 
0.4%
Other values (8869)14395
94.8%
2025-10-22T11:57:28.321137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/75935
 
11.8%
s60748
 
9.5%
w60748
 
9.5%
h30375
 
4.7%
o30375
 
4.7%
t30374
 
4.7%
r30374
 
4.7%
b30374
 
4.7%
.30374
 
4.7%
115240
 
2.4%
Other values (24)246943
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)641860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/75935
 
11.8%
s60748
 
9.5%
w60748
 
9.5%
h30375
 
4.7%
o30375
 
4.7%
t30374
 
4.7%
r30374
 
4.7%
b30374
 
4.7%
.30374
 
4.7%
115240
 
2.4%
Other values (24)246943
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)641860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/75935
 
11.8%
s60748
 
9.5%
w60748
 
9.5%
h30375
 
4.7%
o30375
 
4.7%
t30374
 
4.7%
r30374
 
4.7%
b30374
 
4.7%
.30374
 
4.7%
115240
 
2.4%
Other values (24)246943
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)641860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/75935
 
11.8%
s60748
 
9.5%
w60748
 
9.5%
h30375
 
4.7%
o30375
 
4.7%
t30374
 
4.7%
r30374
 
4.7%
b30374
 
4.7%
.30374
 
4.7%
115240
 
2.4%
Other values (24)246943
38.5%
Distinct3567
Distinct (%)23.5%
Missing2
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:28.538052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length32
Mean length6.830765178
Min length1

Characters and Unicode

Total characters103732
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2007 ?
Unique (%)13.2%

Sample

1st rowSylvia
2nd rowElizabeth
3rd rowTodd
4th rowAmy
5th rowKaren
ValueCountFrequency (%)
and353
 
1.9%
austin284
 
1.6%
259
 
1.4%
michael191
 
1.0%
roompicks181
 
1.0%
david150
 
0.8%
vacasa142
 
0.8%
vacay141
 
0.8%
texas141
 
0.8%
jason129
 
0.7%
Other values (3314)16241
89.2%
2025-10-22T11:57:28.868852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a12092
 
11.7%
e9599
 
9.3%
n8238
 
7.9%
i7705
 
7.4%
r5904
 
5.7%
l5218
 
5.0%
o4715
 
4.5%
s4137
 
4.0%
t4067
 
3.9%
3026
 
2.9%
Other values (66)39031
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)103732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a12092
 
11.7%
e9599
 
9.3%
n8238
 
7.9%
i7705
 
7.4%
r5904
 
5.7%
l5218
 
5.0%
o4715
 
4.5%
s4137
 
4.0%
t4067
 
3.9%
3026
 
2.9%
Other values (66)39031
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)103732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a12092
 
11.7%
e9599
 
9.3%
n8238
 
7.9%
i7705
 
7.4%
r5904
 
5.7%
l5218
 
5.0%
o4715
 
4.5%
s4137
 
4.0%
t4067
 
3.9%
3026
 
2.9%
Other values (66)39031
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)103732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a12092
 
11.7%
e9599
 
9.3%
n8238
 
7.9%
i7705
 
7.4%
r5904
 
5.7%
l5218
 
5.0%
o4715
 
4.5%
s4137
 
4.0%
t4067
 
3.9%
3026
 
2.9%
Other values (66)39031
37.6%
Distinct3882
Distinct (%)25.6%
Missing2
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:29.036963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.999407349
Min length1

Characters and Unicode

Total characters151851
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1207 ?
Unique (%)7.9%

Sample

1st row2009-02-16
2nd row2009-02-19
3rd row2009-04-17
4th row2009-04-20
5th row2009-07-11
ValueCountFrequency (%)
2023-02-20174
 
1.1%
2016-12-16117
 
0.8%
2014-07-17107
 
0.7%
2013-08-1494
 
0.6%
2022-06-1574
 
0.5%
2019-07-0865
 
0.4%
2015-06-1463
 
0.4%
2017-02-2861
 
0.4%
2022-03-0260
 
0.4%
2022-04-2859
 
0.4%
Other values (3872)14312
94.2%
2025-10-22T11:57:29.281738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
035475
23.4%
-30370
20.0%
229779
19.6%
124437
16.1%
35705
 
3.8%
44832
 
3.2%
64818
 
3.2%
54746
 
3.1%
74234
 
2.8%
83763
 
2.5%
Other values (2)3692
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)151851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
035475
23.4%
-30370
20.0%
229779
19.6%
124437
16.1%
35705
 
3.8%
44832
 
3.2%
64818
 
3.2%
54746
 
3.1%
74234
 
2.8%
83763
 
2.5%
Other values (2)3692
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)151851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
035475
23.4%
-30370
20.0%
229779
19.6%
124437
16.1%
35705
 
3.8%
44832
 
3.2%
64818
 
3.2%
54746
 
3.1%
74234
 
2.8%
83763
 
2.5%
Other values (2)3692
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)151851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
035475
23.4%
-30370
20.0%
229779
19.6%
124437
16.1%
35705
 
3.8%
44832
 
3.2%
64818
 
3.2%
54746
 
3.1%
74234
 
2.8%
83763
 
2.5%
Other values (2)3692
 
2.4%

host_location
Text

Missing 

Distinct527
Distinct (%)4.2%
Missing2568
Missing (%)16.9%
Memory size118.8 KiB
2025-10-22T11:57:29.470868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length10
Mean length10.81164818
Min length6

Characters and Unicode

Total characters136443
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique297 ?
Unique (%)2.4%

Sample

1st rowAustin, TX
2nd rowAustin, TX
3rd rowAustin, TX
4th rowAustin, TX
5th rowAustin, TX
ValueCountFrequency (%)
tx10635
39.9%
austin9885
37.1%
ca410
 
1.5%
new331
 
1.2%
ny323
 
1.2%
york307
 
1.2%
san209
 
0.8%
united200
 
0.8%
states176
 
0.7%
angeles131
 
0.5%
Other values (619)4030
 
15.1%
2025-10-22T11:57:29.739128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14017
10.3%
,12558
9.2%
n11980
8.8%
t11438
8.4%
i11291
8.3%
s11266
8.3%
T10831
7.9%
A10770
7.9%
X10635
7.8%
u10378
7.6%
Other values (49)21279
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)136443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14017
10.3%
,12558
9.2%
n11980
8.8%
t11438
8.4%
i11291
8.3%
s11266
8.3%
T10831
7.9%
A10770
7.9%
X10635
7.8%
u10378
7.6%
Other values (49)21279
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)136443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14017
10.3%
,12558
9.2%
n11980
8.8%
t11438
8.4%
i11291
8.3%
s11266
8.3%
T10831
7.9%
A10770
7.9%
X10635
7.8%
u10378
7.6%
Other values (49)21279
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)136443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14017
10.3%
,12558
9.2%
n11980
8.8%
t11438
8.4%
i11291
8.3%
s11266
8.3%
T10831
7.9%
A10770
7.9%
X10635
7.8%
u10378
7.6%
Other values (49)21279
15.6%

host_about
Text

Missing 

Distinct4791
Distinct (%)51.2%
Missing5836
Missing (%)38.4%
Memory size118.8 KiB
2025-10-22T11:57:29.938634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5830
Median length825
Mean length336.4140291
Min length1

Characters and Unicode

Total characters3146144
Distinct characters330
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3601 ?
Unique (%)38.5%

Sample

1st rowI am a licensed Real Estate Broker and owner of Armadillo Realty. I attended The University of Texas at Austin and fell in love with the small town that it was back in 1979; I have been here every since. I love the Art, Music and Film scene here in Austin. There is so much natural beauty to enjoy as well. I especially enjoy Barton Springs Pool in the summertime along with the Zilker Hillside theater productions. SXSW, Austin City Limits Festival and the East Austin Art Studio Tour are among my favorite events. I also enjoy a sunset cruise on my canoe to Congress bridge to see the Mexican Freetail Bats come out for their nightly feeding.
2nd rowWe're easygoing professionals that enjoy meeting new people. I love martial arts, the outdoors, kayaking, live music, good food and positive people. I can converse in Spanish and can cook a mean Mexican dinner.
3rd rowWe're a young family that likes to travel, we just don't get to enough. So we live vicariously through our visiting guests. We run this little vacation rental apartment ourselves. As mentioned in the listing, it's located on our property. It's an above garage apartment that is completely separate from our home. We like to think it's a bit European to share our place with guests. We're very sensitive to guests' needs and give you plenty of space. We're happy to answer any questions about Austin, help you around town, make suggestions, and more if you'd like. We've lived in Austin for about 20 years and have been renting the studio for more than nine years and make improvements as we grow with it. Thank you for considering our place. PS. No, our profile pic was not taken in Austin:)
4th rowWe are a family of four (with teenagers, all of us vaccinated). We love our home town and location... can't beat the park, river, and downtown. We love having guests in our garage apartment... it's fun to talk to people about their lives, hometowns, and travels. We're happy to give recommendations and want you to be comfy.
5th rowI handle the reservations at the studio on the lower level of a house that belongs to a good friend of mine. I really enjoy this part of town, and it is great to be offering comfortable & homey lodging for people coming from around the world to experience Austin.
ValueCountFrequency (%)
and25977
 
4.8%
to18667
 
3.4%
a13812
 
2.5%
the13734
 
2.5%
i13260
 
2.4%
in11435
 
2.1%
we8742
 
1.6%
of8520
 
1.6%
my6592
 
1.2%
for6485
 
1.2%
Other values (12692)416969
76.6%
2025-10-22T11:57:30.262291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
538657
17.1%
e292754
 
9.3%
a204182
 
6.5%
o201841
 
6.4%
t193435
 
6.1%
n182088
 
5.8%
i165845
 
5.3%
r152816
 
4.9%
s147075
 
4.7%
l106259
 
3.4%
Other values (320)961192
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)3146144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
538657
17.1%
e292754
 
9.3%
a204182
 
6.5%
o201841
 
6.4%
t193435
 
6.1%
n182088
 
5.8%
i165845
 
5.3%
r152816
 
4.9%
s147075
 
4.7%
l106259
 
3.4%
Other values (320)961192
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3146144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
538657
17.1%
e292754
 
9.3%
a204182
 
6.5%
o201841
 
6.4%
t193435
 
6.1%
n182088
 
5.8%
i165845
 
5.3%
r152816
 
4.9%
s147075
 
4.7%
l106259
 
3.4%
Other values (320)961192
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3146144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
538657
17.1%
e292754
 
9.3%
a204182
 
6.5%
o201841
 
6.4%
t193435
 
6.1%
n182088
 
5.8%
i165845
 
5.3%
r152816
 
4.9%
s147075
 
4.7%
l106259
 
3.4%
Other values (320)961192
30.6%

host_response_time
Text

Missing 

Distinct4
Distinct (%)< 0.1%
Missing4321
Missing (%)28.5%
Memory size118.8 KiB
2025-10-22T11:57:30.337975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length14
Mean length14.36643048
Min length12

Characters and Unicode

Total characters156120
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwithin a few hours
2nd rowwithin an hour
3rd rowwithin an hour
4th rowwithin an hour
5th rowwithin a day
ValueCountFrequency (%)
within10665
31.3%
an9048
26.6%
hour9048
26.6%
a1819
 
5.3%
few1270
 
3.7%
hours1068
 
3.1%
day549
 
1.6%
days202
 
0.6%
or202
 
0.6%
more202
 
0.6%
2025-10-22T11:57:30.493523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23206
14.9%
i21330
13.7%
h20781
13.3%
n19713
12.6%
w11935
7.6%
a11618
7.4%
t10665
6.8%
o10520
6.7%
r10520
6.7%
u10116
6.5%
Other values (6)5716
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)156120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
23206
14.9%
i21330
13.7%
h20781
13.3%
n19713
12.6%
w11935
7.6%
a11618
7.4%
t10665
6.8%
o10520
6.7%
r10520
6.7%
u10116
6.5%
Other values (6)5716
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)156120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
23206
14.9%
i21330
13.7%
h20781
13.3%
n19713
12.6%
w11935
7.6%
a11618
7.4%
t10665
6.8%
o10520
6.7%
r10520
6.7%
u10116
6.5%
Other values (6)5716
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)156120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
23206
14.9%
i21330
13.7%
h20781
13.3%
n19713
12.6%
w11935
7.6%
a11618
7.4%
t10665
6.8%
o10520
6.7%
r10520
6.7%
u10116
6.5%
Other values (6)5716
 
3.7%

host_response_rate
Text

Missing 

Distinct44
Distinct (%)0.4%
Missing4320
Missing (%)28.4%
Memory size118.8 KiB
2025-10-22T11:57:30.577888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.832075819
Min length2

Characters and Unicode

Total characters41647
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row100%
2nd row100%
3rd row100%
4th row100%
5th row80%
ValueCountFrequency (%)
1009175
84.4%
99293
 
2.7%
98222
 
2.0%
97144
 
1.3%
0135
 
1.2%
95127
 
1.2%
90124
 
1.1%
96105
 
1.0%
8068
 
0.6%
9166
 
0.6%
Other values (34)409
 
3.8%
2025-10-22T11:57:30.741069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
018777
45.1%
%10867
26.1%
19262
22.2%
91489
 
3.6%
8416
 
1.0%
7259
 
0.6%
5228
 
0.5%
6166
 
0.4%
371
 
0.2%
261
 
0.1%
Other values (2)51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)41647
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
018777
45.1%
%10867
26.1%
19262
22.2%
91489
 
3.6%
8416
 
1.0%
7259
 
0.6%
5228
 
0.5%
6166
 
0.4%
371
 
0.2%
261
 
0.1%
Other values (2)51
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41647
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
018777
45.1%
%10867
26.1%
19262
22.2%
91489
 
3.6%
8416
 
1.0%
7259
 
0.6%
5228
 
0.5%
6166
 
0.4%
371
 
0.2%
261
 
0.1%
Other values (2)51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41647
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
018777
45.1%
%10867
26.1%
19262
22.2%
91489
 
3.6%
8416
 
1.0%
7259
 
0.6%
5228
 
0.5%
6166
 
0.4%
371
 
0.2%
261
 
0.1%
Other values (2)51
 
0.1%

host_acceptance_rate
Text

Missing 

Distinct97
Distinct (%)0.8%
Missing3500
Missing (%)23.0%
Memory size118.8 KiB
2025-10-22T11:57:30.880216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.390143737
Min length2

Characters and Unicode

Total characters39624
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row92%
2nd row100%
3rd row100%
4th row96%
5th row50%
ValueCountFrequency (%)
1004963
42.5%
991148
 
9.8%
98782
 
6.7%
97555
 
4.7%
96485
 
4.1%
0384
 
3.3%
67216
 
1.8%
95179
 
1.5%
92166
 
1.4%
87153
 
1.3%
Other values (87)2657
22.7%
2025-10-22T11:57:31.091672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
%11687
29.5%
010861
27.4%
15320
13.4%
95183
13.1%
81855
 
4.7%
71401
 
3.5%
61136
 
2.9%
5784
 
2.0%
3521
 
1.3%
2440
 
1.1%
Other values (3)436
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)39624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
%11687
29.5%
010861
27.4%
15320
13.4%
95183
13.1%
81855
 
4.7%
71401
 
3.5%
61136
 
2.9%
5784
 
2.0%
3521
 
1.3%
2440
 
1.1%
Other values (3)436
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)39624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
%11687
29.5%
010861
27.4%
15320
13.4%
95183
13.1%
81855
 
4.7%
71401
 
3.5%
61136
 
2.9%
5784
 
2.0%
3521
 
1.3%
2440
 
1.1%
Other values (3)436
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)39624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
%11687
29.5%
010861
27.4%
15320
13.4%
95183
13.1%
81855
 
4.7%
71401
 
3.5%
61136
 
2.9%
5784
 
2.0%
3521
 
1.3%
2440
 
1.1%
Other values (3)436
 
1.1%

host_is_superhost
Text

Missing 

Distinct3
Distinct (%)< 0.1%
Missing479
Missing (%)3.2%
Memory size118.8 KiB
2025-10-22T11:57:31.148834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length1
Mean length1.000747841
Min length1

Characters and Unicode

Total characters14720
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowt
2nd rowf
3rd rowt
4th rowt
5th rowf
ValueCountFrequency (%)
f8856
60.2%
t5852
39.8%
entire1
 
< 0.1%
condo1
 
< 0.1%
2025-10-22T11:57:31.273761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f8856
60.2%
t5853
39.8%
n2
 
< 0.1%
o2
 
< 0.1%
E1
 
< 0.1%
r1
 
< 0.1%
i1
 
< 0.1%
e1
 
< 0.1%
1
 
< 0.1%
c1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)14720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f8856
60.2%
t5853
39.8%
n2
 
< 0.1%
o2
 
< 0.1%
E1
 
< 0.1%
r1
 
< 0.1%
i1
 
< 0.1%
e1
 
< 0.1%
1
 
< 0.1%
c1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f8856
60.2%
t5853
39.8%
n2
 
< 0.1%
o2
 
< 0.1%
E1
 
< 0.1%
r1
 
< 0.1%
i1
 
< 0.1%
e1
 
< 0.1%
1
 
< 0.1%
c1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f8856
60.2%
t5853
39.8%
n2
 
< 0.1%
o2
 
< 0.1%
E1
 
< 0.1%
r1
 
< 0.1%
i1
 
< 0.1%
e1
 
< 0.1%
1
 
< 0.1%
c1
 
< 0.1%
Distinct8701
Distinct (%)57.3%
Missing3
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:31.400037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length131
Median length106
Mean length110.566085
Min length15

Characters and Unicode

Total characters1678946
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6877 ?
Unique (%)45.3%

Sample

1st rowhttps://a0.muscache.com/im/users/8028/profile_pic/1329882962/original.jpg?aki_policy=profile_small
2nd rowhttps://a0.muscache.com/im/users/8186/profile_pic/1272556663/original.jpg?aki_policy=profile_small
3rd rowhttps://a0.muscache.com/im/pictures/user/4f35ef11-7f37-45cf-80da-f914a6d5f451.jpg?aki_policy=profile_small
4th rowhttps://a0.muscache.com/im/users/14156/profile_pic/1413388190/original.jpg?aki_policy=profile_small
5th rowhttps://a0.muscache.com/im/users/25298/profile_pic/1330879914/original.jpg?aki_policy=profile_small
ValueCountFrequency (%)
https://a0.muscache.com/defaults/user_pic-50x50.png?v=3224
 
1.5%
https://a0.muscache.com/im/pictures/user/d0ad9599-6fc0-4be6-865e-ffe99142517c.jpg?aki_policy=profile_small116
 
0.8%
https://a0.muscache.com/im/pictures/user/ba5ed72c-8e91-47b1-861f-02c219ddbd8d.jpg?aki_policy=profile_small97
 
0.6%
https://a0.muscache.com/im/users/8167447/profile_pic/1376533837/original.jpg?aki_policy=profile_small94
 
0.6%
https://a0.muscache.com/im/pictures/user/878362e2-a2de-4cea-a340-58d2bac8641e.jpg?aki_policy=profile_small90
 
0.6%
https://a0.muscache.com/im/pictures/user/b3450012-4b09-455a-a3e2-88f517994f55.jpg?aki_policy=profile_small83
 
0.5%
https://a0.muscache.com/im/pictures/user/d1cefa63-f411-43ef-bd90-76567aa117ba.jpg?aki_policy=profile_small73
 
0.5%
https://a0.muscache.com/im/pictures/user/user-274552333/original/e1642ef3-60d6-4d07-92cb-79ffd652cdfc.png?aki_policy=profile_small65
 
0.4%
https://a0.muscache.com/im/pictures/user/df95b3cf-e4af-4254-b660-60c919365716.jpg?aki_policy=profile_small60
 
0.4%
https://a0.muscache.com/im/pictures/user/user-456475914/original/1845c16d-2ce8-45f9-9246-96c26cdcc3aa.png?aki_policy=profile_small59
 
0.4%
Other values (8692)14225
93.7%
2025-10-22T11:57:31.614386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/103007
 
6.1%
c100398
 
6.0%
a94285
 
5.6%
e93919
 
5.6%
i89140
 
5.3%
s80106
 
4.8%
p77238
 
4.6%
l68004
 
4.1%
m60271
 
3.6%
r55690
 
3.3%
Other values (34)856888
51.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1678946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/103007
 
6.1%
c100398
 
6.0%
a94285
 
5.6%
e93919
 
5.6%
i89140
 
5.3%
s80106
 
4.8%
p77238
 
4.6%
l68004
 
4.1%
m60271
 
3.6%
r55690
 
3.3%
Other values (34)856888
51.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1678946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/103007
 
6.1%
c100398
 
6.0%
a94285
 
5.6%
e93919
 
5.6%
i89140
 
5.3%
s80106
 
4.8%
p77238
 
4.6%
l68004
 
4.1%
m60271
 
3.6%
r55690
 
3.3%
Other values (34)856888
51.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1678946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/103007
 
6.1%
c100398
 
6.0%
a94285
 
5.6%
e93919
 
5.6%
i89140
 
5.3%
s80106
 
4.8%
p77238
 
4.6%
l68004
 
4.1%
m60271
 
3.6%
r55690
 
3.3%
Other values (34)856888
51.0%
Distinct8701
Distinct (%)57.3%
Missing3
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:31.741959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length134
Median length109
Mean length113.550214
Min length1

Characters and Unicode

Total characters1724260
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6877 ?
Unique (%)45.3%

Sample

1st rowhttps://a0.muscache.com/im/users/8028/profile_pic/1329882962/original.jpg?aki_policy=profile_x_medium
2nd rowhttps://a0.muscache.com/im/users/8186/profile_pic/1272556663/original.jpg?aki_policy=profile_x_medium
3rd rowhttps://a0.muscache.com/im/pictures/user/4f35ef11-7f37-45cf-80da-f914a6d5f451.jpg?aki_policy=profile_x_medium
4th rowhttps://a0.muscache.com/im/users/14156/profile_pic/1413388190/original.jpg?aki_policy=profile_x_medium
5th rowhttps://a0.muscache.com/im/users/25298/profile_pic/1330879914/original.jpg?aki_policy=profile_x_medium
ValueCountFrequency (%)
https://a0.muscache.com/defaults/user_pic-225x225.png?v=3224
 
1.5%
https://a0.muscache.com/im/pictures/user/d0ad9599-6fc0-4be6-865e-ffe99142517c.jpg?aki_policy=profile_x_medium116
 
0.8%
https://a0.muscache.com/im/pictures/user/ba5ed72c-8e91-47b1-861f-02c219ddbd8d.jpg?aki_policy=profile_x_medium97
 
0.6%
https://a0.muscache.com/im/users/8167447/profile_pic/1376533837/original.jpg?aki_policy=profile_x_medium94
 
0.6%
https://a0.muscache.com/im/pictures/user/878362e2-a2de-4cea-a340-58d2bac8641e.jpg?aki_policy=profile_x_medium90
 
0.6%
https://a0.muscache.com/im/pictures/user/b3450012-4b09-455a-a3e2-88f517994f55.jpg?aki_policy=profile_x_medium83
 
0.5%
https://a0.muscache.com/im/pictures/user/d1cefa63-f411-43ef-bd90-76567aa117ba.jpg?aki_policy=profile_x_medium73
 
0.5%
https://a0.muscache.com/im/pictures/user/user-274552333/original/e1642ef3-60d6-4d07-92cb-79ffd652cdfc.png?aki_policy=profile_x_medium65
 
0.4%
https://a0.muscache.com/im/pictures/user/df95b3cf-e4af-4254-b660-60c919365716.jpg?aki_policy=profile_x_medium60
 
0.4%
https://a0.muscache.com/im/pictures/user/user-456475914/original/1845c16d-2ce8-45f9-9246-96c26cdcc3aa.png?aki_policy=profile_x_medium59
 
0.4%
Other values (8691)14224
93.7%
2025-10-22T11:57:31.958473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e108877
 
6.3%
i104099
 
6.0%
/103006
 
6.0%
c100398
 
5.8%
a79324
 
4.6%
p77237
 
4.5%
m75230
 
4.4%
s65146
 
3.8%
u58747
 
3.4%
r55689
 
3.2%
Other values (32)896507
52.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1724260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e108877
 
6.3%
i104099
 
6.0%
/103006
 
6.0%
c100398
 
5.8%
a79324
 
4.6%
p77237
 
4.5%
m75230
 
4.4%
s65146
 
3.8%
u58747
 
3.4%
r55689
 
3.2%
Other values (32)896507
52.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1724260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e108877
 
6.3%
i104099
 
6.0%
/103006
 
6.0%
c100398
 
5.8%
a79324
 
4.6%
p77237
 
4.5%
m75230
 
4.4%
s65146
 
3.8%
u58747
 
3.4%
r55689
 
3.2%
Other values (32)896507
52.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1724260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e108877
 
6.3%
i104099
 
6.0%
/103006
 
6.0%
c100398
 
5.8%
a79324
 
4.6%
p77237
 
4.5%
m75230
 
4.4%
s65146
 
3.8%
u58747
 
3.4%
r55689
 
3.2%
Other values (32)896507
52.0%

host_neighbourhood
Text

Missing 

Distinct734
Distinct (%)5.4%
Missing1630
Missing (%)10.7%
Memory size118.8 KiB
2025-10-22T11:57:32.141152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length32
Mean length13.03510842
Min length3

Characters and Unicode

Total characters176730
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique271 ?
Unique (%)2.0%

Sample

1st rowEast Downtown
2nd rowSW Williamson Co.
3rd rowTravis Heights
4th rowZilker
5th rowEast Riverside
ValueCountFrequency (%)
austin1993
 
7.4%
south1464
 
5.5%
east1374
 
5.1%
downtown1125
 
4.2%
north642
 
2.4%
creek627
 
2.3%
central625
 
2.3%
hills500
 
1.9%
west470
 
1.8%
park448
 
1.7%
Other values (682)17559
65.5%
2025-10-22T11:57:32.445864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t14485
 
8.2%
13270
 
7.5%
e12850
 
7.3%
o12673
 
7.2%
s11530
 
6.5%
i11527
 
6.5%
r10821
 
6.1%
n10767
 
6.1%
a10297
 
5.8%
l8438
 
4.8%
Other values (57)60072
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)176730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t14485
 
8.2%
13270
 
7.5%
e12850
 
7.3%
o12673
 
7.2%
s11530
 
6.5%
i11527
 
6.5%
r10821
 
6.1%
n10767
 
6.1%
a10297
 
5.8%
l8438
 
4.8%
Other values (57)60072
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)176730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t14485
 
8.2%
13270
 
7.5%
e12850
 
7.3%
o12673
 
7.2%
s11530
 
6.5%
i11527
 
6.5%
r10821
 
6.1%
n10767
 
6.1%
a10297
 
5.8%
l8438
 
4.8%
Other values (57)60072
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)176730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t14485
 
8.2%
13270
 
7.5%
e12850
 
7.3%
o12673
 
7.2%
s11530
 
6.5%
i11527
 
6.5%
r10821
 
6.1%
n10767
 
6.1%
a10297
 
5.8%
l8438
 
4.8%
Other values (57)60072
34.0%
Distinct156
Distinct (%)1.0%
Missing3
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:32.633711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.369772802
Min length3

Characters and Unicode

Total characters51170
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.06086
40.1%
2.01916
 
12.6%
3.01150
 
7.6%
4.0664
 
4.4%
5.0457
 
3.0%
6.0307
 
2.0%
8.0223
 
1.5%
7.0217
 
1.4%
9.0148
 
1.0%
11.0145
 
1.0%
Other values (147)3873
25.5%
2025-10-22T11:57:32.890212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
015689
30.7%
.15184
29.7%
18118
15.9%
23053
 
6.0%
32242
 
4.4%
41757
 
3.4%
81182
 
2.3%
51108
 
2.2%
61064
 
2.1%
9941
 
1.8%
Other values (6)832
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)51170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
015689
30.7%
.15184
29.7%
18118
15.9%
23053
 
6.0%
32242
 
4.4%
41757
 
3.4%
81182
 
2.3%
51108
 
2.2%
61064
 
2.1%
9941
 
1.8%
Other values (6)832
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)51170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
015689
30.7%
.15184
29.7%
18118
15.9%
23053
 
6.0%
32242
 
4.4%
41757
 
3.4%
81182
 
2.3%
51108
 
2.2%
61064
 
2.1%
9941
 
1.8%
Other values (6)832
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)51170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
015689
30.7%
.15184
29.7%
18118
15.9%
23053
 
6.0%
32242
 
4.4%
41757
 
3.4%
81182
 
2.3%
51108
 
2.2%
61064
 
2.1%
9941
 
1.8%
Other values (6)832
 
1.6%

host_total_listings_count
Real number (ℝ)

Distinct192
Distinct (%)1.3%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean183.4021073
Minimum1
Maximum8202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:32.985193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q315
95-th percentile728
Maximum8202
Range8201
Interquartile range (IQR)14

Descriptive statistics

Standard deviation858.9307377
Coefficient of variation (CV)4.683319893
Kurtosis36.59383234
Mean183.4021073
Median Absolute Deviation (MAD)2
Skewness5.996506314
Sum2784961
Variance737762.0122
MonotonicityNot monotonic
2025-10-22T11:57:33.085640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14215
27.8%
22294
15.1%
31413
 
9.3%
4907
 
6.0%
5601
 
4.0%
6417
 
2.7%
7328
 
2.2%
8257
 
1.7%
9222
 
1.5%
11175
 
1.2%
Other values (182)4356
28.7%
ValueCountFrequency (%)
14215
27.8%
22294
15.1%
31413
 
9.3%
4907
 
6.0%
5601
 
4.0%
ValueCountFrequency (%)
82025
 
< 0.1%
656712
 
0.1%
639790
0.6%
5690116
0.8%
470883
0.5%
Distinct9
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:33.170683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length48
Median length18
Mean length19.57951926
Min length2

Characters and Unicode

Total characters297315
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row['email', 'phone']
2nd row['email', 'phone', 'work_email']
3rd row['email', 'phone']
4th row['email', 'phone']
5th row['email', 'phone']
ValueCountFrequency (%)
phone15163
47.8%
email14175
44.7%
work_email2375
 
7.5%
2
 
< 0.1%
photographer1
 
< 0.1%
1.01
 
< 0.1%
2025-10-22T11:57:33.317098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'63428
21.3%
e31714
10.7%
o17540
 
5.9%
a16551
 
5.6%
i16550
 
5.6%
l16550
 
5.6%
m16550
 
5.6%
16532
 
5.6%
,16532
 
5.6%
[15184
 
5.1%
Other values (13)70184
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)297315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'63428
21.3%
e31714
10.7%
o17540
 
5.9%
a16551
 
5.6%
i16550
 
5.6%
l16550
 
5.6%
m16550
 
5.6%
16532
 
5.6%
,16532
 
5.6%
[15184
 
5.1%
Other values (13)70184
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)297315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'63428
21.3%
e31714
10.7%
o17540
 
5.9%
a16551
 
5.6%
i16550
 
5.6%
l16550
 
5.6%
m16550
 
5.6%
16532
 
5.6%
,16532
 
5.6%
[15184
 
5.1%
Other values (13)70184
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)297315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'63428
21.3%
e31714
10.7%
o17540
 
5.9%
a16551
 
5.6%
i16550
 
5.6%
l16550
 
5.6%
m16550
 
5.6%
16532
 
5.6%
,16532
 
5.6%
[15184
 
5.1%
Other values (13)70184
23.6%
Distinct3
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:33.450085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1089
Median length1
Mean length1.071649654
Min length1

Characters and Unicode

Total characters16273
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowt
2nd rowt
3rd rowt
4th rowt
5th rowt
ValueCountFrequency (%)
t14960
97.7%
f224
 
1.5%
free4
 
< 0.1%
shared3
 
< 0.1%
in3
 
< 0.1%
outdoor3
 
< 0.1%
alarm2
 
< 0.1%
u20132
 
< 0.1%
and2
 
< 0.1%
pool2
 
< 0.1%
Other values (104)111
 
0.7%
2025-10-22T11:57:33.673691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t15014
92.3%
f229
 
1.4%
131
 
0.8%
"114
 
0.7%
e89
 
0.5%
r62
 
0.4%
a60
 
0.4%
i60
 
0.4%
o59
 
0.4%
,56
 
0.3%
Other values (48)399
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)16273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t15014
92.3%
f229
 
1.4%
131
 
0.8%
"114
 
0.7%
e89
 
0.5%
r62
 
0.4%
a60
 
0.4%
i60
 
0.4%
o59
 
0.4%
,56
 
0.3%
Other values (48)399
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t15014
92.3%
f229
 
1.4%
131
 
0.8%
"114
 
0.7%
e89
 
0.5%
r62
 
0.4%
a60
 
0.4%
i60
 
0.4%
o59
 
0.4%
,56
 
0.3%
Other values (48)399
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t15014
92.3%
f229
 
1.4%
131
 
0.8%
"114
 
0.7%
e89
 
0.5%
r62
 
0.4%
a60
 
0.4%
i60
 
0.4%
o59
 
0.4%
,56
 
0.3%
Other values (48)399
 
2.5%
Distinct3
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:33.726429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.000197563
Min length1

Characters and Unicode

Total characters15188
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowt
2nd rowt
3rd rowt
4th rowt
5th rowf
ValueCountFrequency (%)
t13015
85.7%
f2169
 
14.3%
86.01
 
< 0.1%
2025-10-22T11:57:33.844223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t13015
85.7%
f2169
 
14.3%
81
 
< 0.1%
61
 
< 0.1%
.1
 
< 0.1%
01
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t13015
85.7%
f2169
 
14.3%
81
 
< 0.1%
61
 
< 0.1%
.1
 
< 0.1%
01
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t13015
85.7%
f2169
 
14.3%
81
 
< 0.1%
61
 
< 0.1%
.1
 
< 0.1%
01
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t13015
85.7%
f2169
 
14.3%
81
 
< 0.1%
61
 
< 0.1%
.1
 
< 0.1%
01
 
< 0.1%

neighbourhood
Text

Missing 

Distinct2
Distinct (%)< 0.1%
Missing7273
Missing (%)47.9%
Memory size118.8 KiB
2025-10-22T11:57:33.920019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length23
Mean length22.99734681
Min length2

Characters and Unicode

Total characters182024
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNeighborhood highlights
2nd rowNeighborhood highlights
3rd rowNeighborhood highlights
4th rowNeighborhood highlights
5th rowNeighborhood highlights
ValueCountFrequency (%)
neighborhood7914
50.0%
highlights7914
50.0%
301
 
< 0.1%
2025-10-22T11:57:34.056899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h39570
21.7%
g23742
13.0%
i23742
13.0%
o23742
13.0%
N7914
 
4.3%
e7914
 
4.3%
b7914
 
4.3%
r7914
 
4.3%
d7914
 
4.3%
7914
 
4.3%
Other values (5)23744
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)182024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h39570
21.7%
g23742
13.0%
i23742
13.0%
o23742
13.0%
N7914
 
4.3%
e7914
 
4.3%
b7914
 
4.3%
r7914
 
4.3%
d7914
 
4.3%
7914
 
4.3%
Other values (5)23744
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)182024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h39570
21.7%
g23742
13.0%
i23742
13.0%
o23742
13.0%
N7914
 
4.3%
e7914
 
4.3%
b7914
 
4.3%
r7914
 
4.3%
d7914
 
4.3%
7914
 
4.3%
Other values (5)23744
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)182024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h39570
21.7%
g23742
13.0%
i23742
13.0%
o23742
13.0%
N7914
 
4.3%
e7914
 
4.3%
b7914
 
4.3%
r7914
 
4.3%
d7914
 
4.3%
7914
 
4.3%
Other values (5)23744
13.0%

neighbourhood_cleansed
Real number (ℝ)

Skewed 

Distinct45
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean78719.21762
Minimum365
Maximum78759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:34.133373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum365
5-th percentile78701
Q178704
median78723
Q378745
95-th percentile78757
Maximum78759
Range78394
Interquartile range (IQR)41

Descriptive statistics

Standard deviation636.1907405
Coefficient of variation (CV)0.008081771641
Kurtosis15154.5258
Mean78719.21762
Median Absolute Deviation (MAD)20
Skewness-123.037865
Sum1195508758
Variance404738.6584
MonotonicityNot monotonic
2025-10-22T11:57:34.234084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
787042284
15.0%
787021773
 
11.7%
787011246
 
8.2%
78741859
 
5.7%
78745834
 
5.5%
78703656
 
4.3%
78705614
 
4.0%
78723487
 
3.2%
78744486
 
3.2%
78751479
 
3.2%
Other values (35)5469
36.0%
ValueCountFrequency (%)
3651
 
< 0.1%
787011246
8.2%
787021773
11.7%
78703656
 
4.3%
787042284
15.0%
ValueCountFrequency (%)
78759181
1.2%
78758448
2.9%
78757293
1.9%
78756190
1.3%
78754171
 
1.1%

neighbourhood_group_cleansed
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing15187
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean18
Minimum18
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:34.330328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q118
median18
Q318
95-th percentile18
Maximum18
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean18
Median Absolute Deviation (MAD)0
Skewnessnan
Sum18
Variancenan
MonotonicityStrictly increasing
2025-10-22T11:57:34.384397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
181
 
< 0.1%
(Missing)15187
> 99.9%
ValueCountFrequency (%)
181
< 0.1%
ValueCountFrequency (%)
181
< 0.1%

latitude
Real number (ℝ)

Distinct12079
Distinct (%)79.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.28032824
Minimum30
Maximum30.51835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:34.465299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30.19129968
Q130.2418992
median30.2692387
Q330.30921
95-th percentile30.410922
Maximum30.51835
Range0.51835
Interquartile range (IQR)0.0673108

Descriptive statistics

Standard deviation0.06422327514
Coefficient of variation (CV)0.002120957033
Kurtosis0.7867470793
Mean30.28032824
Median Absolute Deviation (MAD)0.03111904995
Skewness0.75915655
Sum459867.345
Variance0.00412462907
MonotonicityNot monotonic
2025-10-22T11:57:34.574732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.274881272
 
0.5%
30.3746237
 
0.2%
30.271622834
 
0.2%
30.2715984329
 
0.2%
30.375744125
 
0.2%
30.2690525
 
0.2%
30.3219722
 
0.1%
30.2616918
 
0.1%
30.254350816
 
0.1%
30.328963616
 
0.1%
Other values (12069)14893
98.1%
ValueCountFrequency (%)
301
< 0.1%
30.078441
< 0.1%
30.114831
< 0.1%
30.118791
< 0.1%
30.122363241
< 0.1%
ValueCountFrequency (%)
30.518351
< 0.1%
30.512251
< 0.1%
30.510467041
< 0.1%
30.509940711
< 0.1%
30.509711
< 0.1%

longitude
Real number (ℝ)

Skewed 

Distinct11774
Distinct (%)77.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-97.71989226
Minimum-98.05335
Maximum365
Zeros0
Zeros (%)0.0%
Negative15186
Negative (%)> 99.9%
Memory size118.8 KiB
2025-10-22T11:57:34.679573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-98.05335
5-th percentile-97.90047784
Q1-97.76832
median-97.74142755
Q3-97.71697375
95-th percentile-97.675826
Maximum365
Range463.05335
Interquartile range (IQR)0.05134625

Descriptive statistics

Standard deviation3.755560634
Coefficient of variation (CV)-0.03843189496
Kurtosis15178.04772
Mean-97.71989226
Median Absolute Deviation (MAD)0.02532124836
Skewness123.1810562
Sum-1484072.004
Variance14.10423568
MonotonicityNot monotonic
2025-10-22T11:57:34.914621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-97.75719672
 
0.5%
-97.9866237
 
0.2%
-97.747399635
 
0.2%
-97.7475741529
 
0.2%
-97.729247625
 
0.2%
-97.7419924
 
0.2%
-97.7252123
 
0.2%
-97.7229218
 
0.1%
-97.74108917
 
0.1%
-97.73883116
 
0.1%
Other values (11764)14891
98.0%
ValueCountFrequency (%)
-98.053351
< 0.1%
-98.053291
< 0.1%
-98.052961
< 0.1%
-98.043471
< 0.1%
-98.0252991
< 0.1%
ValueCountFrequency (%)
3651
< 0.1%
-97.55931
< 0.1%
-97.562441
< 0.1%
-97.56656071
< 0.1%
-97.570191
< 0.1%
Distinct69
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:35.032286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length33
Mean length14.82155791
Min length3

Characters and Unicode

Total characters225095
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st rowEntire guesthouse
2nd rowPrivate room in home
3rd rowEntire guesthouse
4th rowEntire guesthouse
5th rowEntire guest suite
ValueCountFrequency (%)
entire11938
30.4%
home7818
19.9%
rental3400
 
8.7%
unit3400
 
8.7%
room2892
 
7.4%
in2866
 
7.3%
private2313
 
5.9%
condo1215
 
3.1%
guesthouse710
 
1.8%
hotel472
 
1.2%
Other values (44)2273
 
5.8%
2025-10-22T11:57:35.224649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e29038
12.9%
24110
10.7%
n23799
10.6%
t23787
10.6%
i21263
9.4%
r20621
9.2%
o18570
8.2%
E11939
 
5.3%
m10925
 
4.9%
h9521
 
4.2%
Other values (30)31522
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)225095
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e29038
12.9%
24110
10.7%
n23799
10.6%
t23787
10.6%
i21263
9.4%
r20621
9.2%
o18570
8.2%
E11939
 
5.3%
m10925
 
4.9%
h9521
 
4.2%
Other values (30)31522
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)225095
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e29038
12.9%
24110
10.7%
n23799
10.6%
t23787
10.6%
i21263
9.4%
r20621
9.2%
o18570
8.2%
E11939
 
5.3%
m10925
 
4.9%
h9521
 
4.2%
Other values (30)31522
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)225095
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e29038
12.9%
24110
10.7%
n23799
10.6%
t23787
10.6%
i21263
9.4%
r20621
9.2%
o18570
8.2%
E11939
 
5.3%
m10925
 
4.9%
h9521
 
4.2%
Other values (30)31522
14.0%
Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:35.302034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.40587344
Min length4

Characters and Unicode

Total characters218782
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowEntire home/apt
2nd rowPrivate room
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt
ValueCountFrequency (%)
entire12325
40.6%
home/apt12325
40.6%
room2861
 
9.4%
private2604
 
8.6%
hotel172
 
0.6%
shared85
 
0.3%
29.71
 
< 0.1%
2025-10-22T11:57:35.462209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e27511
12.6%
t27426
12.5%
o18219
8.3%
r17875
8.2%
15186
 
6.9%
m15186
 
6.9%
a15014
 
6.9%
i14929
 
6.8%
h12410
 
5.7%
E12325
 
5.6%
Other values (13)42701
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)218782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e27511
12.6%
t27426
12.5%
o18219
8.3%
r17875
8.2%
15186
 
6.9%
m15186
 
6.9%
a15014
 
6.9%
i14929
 
6.8%
h12410
 
5.7%
E12325
 
5.6%
Other values (13)42701
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)218782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e27511
12.6%
t27426
12.5%
o18219
8.3%
r17875
8.2%
15186
 
6.9%
m15186
 
6.9%
a15014
 
6.9%
i14929
 
6.8%
h12410
 
5.7%
E12325
 
5.6%
Other values (13)42701
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)218782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e27511
12.6%
t27426
12.5%
o18219
8.3%
r17875
8.2%
15186
 
6.9%
m15186
 
6.9%
a15014
 
6.9%
i14929
 
6.8%
h12410
 
5.7%
E12325
 
5.6%
Other values (13)42701
19.5%

accommodates
Real number (ℝ)

Skewed 

Distinct17
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.032461974
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:35.535002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile12
Maximum365
Range364
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.502578364
Coefficient of variation (CV)0.8947068824
Kurtosis2689.784017
Mean5.032461974
Median Absolute Deviation (MAD)2
Skewness34.27651483
Sum76428
Variance20.27321192
MonotonicityNot monotonic
2025-10-22T11:57:35.610791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
23735
24.6%
43300
21.7%
62283
15.0%
81145
 
7.5%
31042
 
6.9%
1767
 
5.1%
5757
 
5.0%
10541
 
3.6%
16465
 
3.1%
7349
 
2.3%
Other values (7)803
 
5.3%
ValueCountFrequency (%)
1767
 
5.1%
23735
24.6%
31042
 
6.9%
43300
21.7%
5757
 
5.0%
ValueCountFrequency (%)
3651
 
< 0.1%
16465
3.1%
1551
 
0.3%
14156
 
1.0%
1344
 
0.3%

bathrooms
Real number (ℝ)

Missing 

Distinct24
Distinct (%)0.2%
Missing4475
Missing (%)29.5%
Infinite0
Infinite (%)0.0%
Mean1.738448614
Minimum0
Maximum17
Zeros52
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:35.694362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3.5
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.046667469
Coefficient of variation (CV)0.6020698344
Kurtosis13.8139357
Mean1.738448614
Median Absolute Deviation (MAD)0.5
Skewness2.538107686
Sum18624
Variance1.095512791
MonotonicityNot monotonic
2025-10-22T11:57:35.777690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15331
35.1%
22445
16.1%
2.5925
 
6.1%
3635
 
4.2%
1.5542
 
3.6%
3.5277
 
1.8%
4196
 
1.3%
4.589
 
0.6%
572
 
0.5%
052
 
0.3%
Other values (14)149
 
1.0%
(Missing)4475
29.5%
ValueCountFrequency (%)
052
 
0.3%
0.525
 
0.2%
15331
35.1%
1.5542
 
3.6%
22445
16.1%
ValueCountFrequency (%)
171
 
< 0.1%
131
 
< 0.1%
11.53
< 0.1%
10.51
 
< 0.1%
103
< 0.1%
Distinct36
Distinct (%)0.2%
Missing15
Missing (%)0.1%
Memory size118.8 KiB
2025-10-22T11:57:35.896843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length16
Mean length7.821788704
Min length1

Characters and Unicode

Total characters118680
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row1 bath
2nd row1 shared bath
3rd row1 bath
4th row1 bath
5th row1 bath
ValueCountFrequency (%)
17925
24.5%
bath7925
24.5%
baths7214
22.3%
23361
10.4%
2.51256
 
3.9%
shared1036
 
3.2%
private992
 
3.1%
3816
 
2.5%
1.5795
 
2.5%
3.5338
 
1.0%
Other values (19)682
 
2.1%
2025-10-22T11:57:36.073238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a17233
14.5%
17167
14.5%
h16224
13.7%
t16165
13.6%
b15172
12.8%
18733
7.4%
s8243
6.9%
24617
 
3.9%
52685
 
2.3%
.2568
 
2.2%
Other values (19)9873
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)118680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a17233
14.5%
17167
14.5%
h16224
13.7%
t16165
13.6%
b15172
12.8%
18733
7.4%
s8243
6.9%
24617
 
3.9%
52685
 
2.3%
.2568
 
2.2%
Other values (19)9873
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)118680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a17233
14.5%
17167
14.5%
h16224
13.7%
t16165
13.6%
b15172
12.8%
18733
7.4%
s8243
6.9%
24617
 
3.9%
52685
 
2.3%
.2568
 
2.2%
Other values (19)9873
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)118680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a17233
14.5%
17167
14.5%
h16224
13.7%
t16165
13.6%
b15172
12.8%
18733
7.4%
s8243
6.9%
24617
 
3.9%
52685
 
2.3%
.2568
 
2.2%
Other values (19)9873
8.3%

bedrooms
Real number (ℝ)

Missing  Zeros 

Distinct17
Distinct (%)0.1%
Missing940
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean2.095030882
Minimum0
Maximum23
Zeros616
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:36.138814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.442015315
Coefficient of variation (CV)0.6883026536
Kurtosis9.314617485
Mean2.095030882
Median Absolute Deviation (MAD)1
Skewness1.877779428
Sum29850
Variance2.079408169
MonotonicityNot monotonic
2025-10-22T11:57:36.209190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
15567
36.7%
23435
22.6%
32566
16.9%
41306
 
8.6%
0616
 
4.1%
5441
 
2.9%
6174
 
1.1%
757
 
0.4%
835
 
0.2%
917
 
0.1%
Other values (7)34
 
0.2%
(Missing)940
 
6.2%
ValueCountFrequency (%)
0616
 
4.1%
15567
36.7%
23435
22.6%
32566
16.9%
41306
 
8.6%
ValueCountFrequency (%)
231
 
< 0.1%
153
< 0.1%
143
< 0.1%
135
< 0.1%
127
< 0.1%

beds
Real number (ℝ)

Missing 

Distinct33
Distinct (%)0.3%
Missing4492
Missing (%)29.6%
Infinite0
Infinite (%)0.0%
Mean2.974663426
Minimum0
Maximum132
Zeros140
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:36.300984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum132
Range132
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.992372004
Coefficient of variation (CV)1.005953137
Kurtosis344.8749481
Mean2.974663426
Median Absolute Deviation (MAD)1
Skewness10.2746292
Sum31817
Variance8.954290209
MonotonicityNot monotonic
2025-10-22T11:57:36.390708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
13233
21.3%
22720
17.9%
31795
 
11.8%
41063
 
7.0%
5528
 
3.5%
6366
 
2.4%
7207
 
1.4%
8172
 
1.1%
0140
 
0.9%
9125
 
0.8%
Other values (23)347
 
2.3%
(Missing)4492
29.6%
ValueCountFrequency (%)
0140
 
0.9%
13233
21.3%
22720
17.9%
31795
11.8%
41063
 
7.0%
ValueCountFrequency (%)
1321
< 0.1%
611
< 0.1%
391
< 0.1%
291
< 0.1%
281
< 0.1%
Distinct13936
Distinct (%)91.8%
Missing1
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:36.598722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2388
Median length1306
Mean length714.8448015
Min length2

Characters and Unicode

Total characters10856348
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13499 ?
Unique (%)88.9%

Sample

1st row["Extra pillows and blankets", "Wifi", "Luggage dropoff allowed", "Hair dryer", "Dishes and silverware", "Heating", "Refrigerator", "Shampoo", "Microwave", "Private entrance", "Hot water", "Bed linens", "Air conditioning", "Long term stays allowed", "Kitchen", "Hangers", "Iron", "Smoke alarm", "Self check-in", "Coffee maker", "Exterior security cameras on property", "Patio or balcony", "Keypad", "Backyard", "Essentials", "HDTV with Amazon Prime Video, HBO Max, Hulu, Netflix, Roku"]
2nd row["Extra pillows and blankets", "Wifi", "Hair dryer", "Dishes and silverware", "Pets allowed", "Refrigerator", "Conditioner", "Lock on bedroom door", "Shampoo", "Private backyard", "Microwave", "Free parking on premises", "Single level home", "Portable fans", "Host greets you", "Hot water", "Bed linens", "Wine glasses", "Hangers", "Bathtub", "Iron", "Smoke alarm", "Central air conditioning", "First aid kit", "Fire extinguisher", "Ceiling fan", "Portable heater", "Body soap", "Exterior security cameras on property", "Outdoor dining area", "Toaster", "Outdoor furniture", "Shared patio or balcony", "TV with DVD player", "Essentials", "Cleaning products", "Central heating", "Dining table"]
3rd row["37 inch HDTV with Amazon Prime Video, Apple TV, Disney+, Fire TV, HBO Max, Netflix, premium cable", "Extra pillows and blankets", "Clothing storage: walk-in closet", "Carbon monoxide alarm", "Wifi", "Oven", "Free street parking", "Hair dryer", "Dishes and silverware", "Heating", "Conditioner", "Refrigerator", "Baking sheet", "Shampoo", "Microwave", "Portable fans", "Freezer", "Private entrance", "Hot water", "Bed linens", "Wine glasses", "Long term stays allowed", "Kitchen", "Dove body soap", "Free dryer \u2013 In building", "Dedicated workspace", "Cooking basics", "Gas stove", "Private patio or balcony", "Hangers", "Bathtub", "Iron", "Lake access", "Smoke alarm", "Central air conditioning", "Laundromat nearby", "Free washer \u2013 In building", "Fire extinguisher", "Hot water kettle", "Coffee maker", "Ceiling fan", "Coffee", "Outdoor dining area", "Toaster", "Outdoor furniture", "Books and reading material", "Essentials", "Cleaning products", "Dining table"]
4th row["Extra pillows and blankets", "Carbon monoxide alarm", "Wifi", "Oven", "Free street parking", "Luggage dropoff allowed", "Hair dryer", "Dishes and silverware", "Mini fridge", "Refrigerator", "Conditioner", "Babysitter recommendations", "Baking sheet", "Outlet covers", "Shampoo", "TV", "Stove", "Changing table", "Microwave", "Free parking on premises", "Room-darkening shades", "Ethernet connection", "Private entrance", "Hot water", "Bed linens", "Wine glasses", "Clothing storage: closet and dresser", "Long term stays allowed", "Kitchen", "Shower gel", "Free dryer \u2013 In building", "Dedicated workspace", "Cooking basics", "Private patio or balcony", "Hangers", "Pack \u2019n play/Travel crib", "Iron", "Essentials", "Board games", "Smoke alarm", "Central air conditioning", "Laundromat nearby", "Children\u2019s books and toys", "First aid kit", "Self check-in", "Free washer \u2013 In building", "Fire extinguisher", "Crib", "Hot water kettle", "Coffee maker", "Ceiling fan", "Body soap", "Private backyard \u2013 Fully fenced", "Outdoor dining area", "Toaster", "Outdoor furniture", "Keypad", "Record player", "Cleaning products", "Central heating", "Dining table"]
5th row["Shampoo", "Kitchen", "Wifi", "Free street parking", "Smoke alarm", "Central air conditioning", "Host greets you", "Hot water", "Hair dryer", "Essentials", "Heating", "Pets allowed"]
ValueCountFrequency (%)
free27959
 
2.1%
alarm26400
 
2.0%
and25628
 
2.0%
dryer22569
 
1.7%
on22014
 
1.7%
coffee21242
 
1.6%
parking20859
 
1.6%
hot17647
 
1.3%
private17013
 
1.3%
allowed16744
 
1.3%
Other values (2181)1091966
83.4%
2025-10-22T11:57:36.925464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1295360
 
11.9%
"1195213
 
11.0%
e909081
 
8.4%
r661912
 
6.1%
a624031
 
5.7%
i610519
 
5.6%
,595205
 
5.5%
o519427
 
4.8%
n515460
 
4.7%
t440340
 
4.1%
Other values (71)3489800
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10856348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1295360
 
11.9%
"1195213
 
11.0%
e909081
 
8.4%
r661912
 
6.1%
a624031
 
5.7%
i610519
 
5.6%
,595205
 
5.5%
o519427
 
4.8%
n515460
 
4.7%
t440340
 
4.1%
Other values (71)3489800
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10856348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1295360
 
11.9%
"1195213
 
11.0%
e909081
 
8.4%
r661912
 
6.1%
a624031
 
5.7%
i610519
 
5.6%
,595205
 
5.5%
o519427
 
4.8%
n515460
 
4.7%
t440340
 
4.1%
Other values (71)3489800
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10856348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1295360
 
11.9%
"1195213
 
11.0%
e909081
 
8.4%
r661912
 
6.1%
a624031
 
5.7%
i610519
 
5.6%
,595205
 
5.5%
o519427
 
4.8%
n515460
 
4.7%
t440340
 
4.1%
Other values (71)3489800
32.1%

price
Real number (ℝ)

Missing 

Distinct927
Distinct (%)8.7%
Missing4480
Missing (%)29.5%
Infinite0
Infinite (%)0.0%
Mean231.8365185
Minimum9
Maximum2382.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:37.019838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile45
Q189
median138
Q3235
95-th percentile708
Maximum2382.18
Range2373.18
Interquartile range (IQR)146

Descriptive statistics

Standard deviation320.5249318
Coefficient of variation (CV)1.38254721
Kurtosis23.92007695
Mean231.8365185
Median Absolute Deviation (MAD)60
Skewness4.476581648
Sum2482505.44
Variance102736.2319
MonotonicityNot monotonic
2025-10-22T11:57:37.118882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2382.18108
 
0.7%
9093
 
0.6%
9591
 
0.6%
10091
 
0.6%
8086
 
0.6%
15079
 
0.5%
7978
 
0.5%
6876
 
0.5%
12076
 
0.5%
7576
 
0.5%
Other values (917)9854
64.9%
(Missing)4480
29.5%
ValueCountFrequency (%)
92
 
< 0.1%
101
 
< 0.1%
126
< 0.1%
136
< 0.1%
146
< 0.1%
ValueCountFrequency (%)
2382.18108
0.7%
23581
 
< 0.1%
23491
 
< 0.1%
23231
 
< 0.1%
23151
 
< 0.1%
Distinct74
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:37.247046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length1
Mean length1.153749918
Min length1

Characters and Unicode

Total characters17522
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.2%

Sample

1st row2
2nd row1
3rd row30
4th row3
5th row4
ValueCountFrequency (%)
15491
36.2%
24912
32.3%
31760
 
11.6%
301208
 
8.0%
4296
 
1.9%
28225
 
1.5%
5213
 
1.4%
7194
 
1.3%
31189
 
1.2%
1489
 
0.6%
Other values (64)610
 
4.0%
2025-10-22T11:57:37.433004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15935
33.9%
25332
30.4%
33205
18.3%
01510
 
8.6%
4411
 
2.3%
5312
 
1.8%
8285
 
1.6%
7210
 
1.2%
9163
 
0.9%
6157
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)17522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15935
33.9%
25332
30.4%
33205
18.3%
01510
 
8.6%
4411
 
2.3%
5312
 
1.8%
8285
 
1.6%
7210
 
1.2%
9163
 
0.9%
6157
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15935
33.9%
25332
30.4%
33205
18.3%
01510
 
8.6%
4411
 
2.3%
5312
 
1.8%
8285
 
1.6%
7210
 
1.2%
9163
 
0.9%
6157
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15935
33.9%
25332
30.4%
33205
18.3%
01510
 
8.6%
4411
 
2.3%
5312
 
1.8%
8285
 
1.6%
7210
 
1.2%
9163
 
0.9%
6157
 
0.9%

maximum_nights
Real number (ℝ)

Distinct167
Distinct (%)1.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean461.8399947
Minimum1
Maximum1825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:37.524282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q160
median365
Q31125
95-th percentile1125
Maximum1825
Range1824
Interquartile range (IQR)1065

Descriptive statistics

Standard deviation432.0605226
Coefficient of variation (CV)0.9355199367
Kurtosis-1.168685056
Mean461.8399947
Median Absolute Deviation (MAD)335
Skewness0.6446829748
Sum7013964
Variance186676.2952
MonotonicityNot monotonic
2025-10-22T11:57:37.657188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3654999
32.9%
11254045
26.6%
30770
 
5.1%
90535
 
3.5%
14423
 
2.8%
28407
 
2.7%
60406
 
2.7%
7376
 
2.5%
180319
 
2.1%
730266
 
1.8%
Other values (157)2641
17.4%
ValueCountFrequency (%)
113
 
0.1%
225
 
0.2%
3107
0.7%
4100
0.7%
5161
1.1%
ValueCountFrequency (%)
18251
 
< 0.1%
11254045
26.6%
112416
 
0.1%
11221
 
< 0.1%
11211
 
< 0.1%

minimum_minimum_nights
Real number (ℝ)

Skewed 

Distinct73
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.338644894
Minimum1
Maximum1124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:37.791042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile30
Maximum1124
Range1123
Interquartile range (IQR)2

Descriptive statistics

Standard deviation30.12250258
Coefficient of variation (CV)4.104640982
Kurtosis586.4598933
Mean7.338644894
Median Absolute Deviation (MAD)1
Skewness20.34889274
Sum111452
Variance907.3651619
MonotonicityNot monotonic
2025-10-22T11:57:37.917929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15980
39.4%
24877
32.1%
31514
 
10.0%
301187
 
7.8%
4278
 
1.8%
5226
 
1.5%
7199
 
1.3%
28188
 
1.2%
1495
 
0.6%
3178
 
0.5%
Other values (63)565
 
3.7%
ValueCountFrequency (%)
15980
39.4%
24877
32.1%
31514
 
10.0%
4278
 
1.8%
5226
 
1.5%
ValueCountFrequency (%)
11242
 
< 0.1%
11001
 
< 0.1%
10001
 
< 0.1%
7305
< 0.1%
5002
 
< 0.1%

maximum_minimum_nights
Real number (ℝ)

Distinct84
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10.86738658
Minimum0
Maximum1124
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:38.026815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile30
Maximum1124
Range1124
Interquartile range (IQR)2

Descriptive statistics

Standard deviation40.73098127
Coefficient of variation (CV)3.748001506
Kurtosis197.8276689
Mean10.86738658
Median Absolute Deviation (MAD)1
Skewness11.56165631
Sum165043
Variance1659.012835
MonotonicityNot monotonic
2025-10-22T11:57:38.125984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24528
29.8%
33240
21.3%
12976
19.6%
301296
 
8.5%
41207
 
7.9%
5421
 
2.8%
7279
 
1.8%
28247
 
1.6%
14109
 
0.7%
3192
 
0.6%
Other values (74)792
 
5.2%
ValueCountFrequency (%)
01
 
< 0.1%
12976
19.6%
24528
29.8%
33240
21.3%
41207
 
7.9%
ValueCountFrequency (%)
11242
 
< 0.1%
11001
 
< 0.1%
10001
 
< 0.1%
7305
< 0.1%
5002
 
< 0.1%

minimum_maximum_nights
Real number (ℝ)

Skewed 

Distinct149
Distinct (%)1.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1273239.813
Minimum1
Maximum2147483647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:38.228458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q199
median365
Q31125
95-th percentile1125
Maximum2147483647
Range2147483646
Interquartile range (IQR)1026

Descriptive statistics

Standard deviation52263750.95
Coefficient of variation (CV)41.04784536
Kurtosis1682.999473
Mean1273239.813
Median Absolute Deviation (MAD)360
Skewness41.04604532
Sum1.933669304 × 1010
Variance2.731499663 × 1015
MonotonicityNot monotonic
2025-10-22T11:57:38.333261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11256673
43.9%
3653896
25.7%
30490
 
3.2%
14340
 
2.2%
7317
 
2.1%
90300
 
2.0%
1265
 
1.7%
28245
 
1.6%
60223
 
1.5%
180188
 
1.2%
Other values (139)2250
 
14.8%
ValueCountFrequency (%)
1265
1.7%
2114
0.8%
3122
0.8%
482
 
0.5%
5138
0.9%
ValueCountFrequency (%)
21474836479
 
0.1%
18251
 
< 0.1%
11256673
43.9%
112411
 
0.1%
11221
 
< 0.1%

maximum_maximum_nights
Real number (ℝ)

Distinct167
Distinct (%)1.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5798162.915
Minimum1
Maximum2147483647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:38.595093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q1360
median365
Q31125
95-th percentile1125
Maximum2147483647
Range2147483646
Interquartile range (IQR)765

Descriptive statistics

Standard deviation111432774.5
Coefficient of variation (CV)19.21863462
Kurtosis365.5380708
Mean5798162.915
Median Absolute Deviation (MAD)361
Skewness19.17002693
Sum8.805670019 × 1010
Variance1.241726324 × 1016
MonotonicityNot monotonic
2025-10-22T11:57:38.701397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11257131
47.0%
3653874
25.5%
30480
 
3.2%
14334
 
2.2%
7314
 
2.1%
90292
 
1.9%
60219
 
1.4%
28212
 
1.4%
180184
 
1.2%
10171
 
1.1%
Other values (157)1976
 
13.0%
ValueCountFrequency (%)
19
 
0.1%
224
 
0.2%
396
0.6%
481
0.5%
5136
0.9%
ValueCountFrequency (%)
214748364741
 
0.3%
18251
 
< 0.1%
11257131
47.0%
112410
 
0.1%
11221
 
< 0.1%

minimum_nights_avg_ntm
Real number (ℝ)

Distinct313
Distinct (%)2.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean8.322776058
Minimum1
Maximum1124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:38.804365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.3
median2
Q33
95-th percentile30
Maximum1124
Range1123
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation30.57662059
Coefficient of variation (CV)3.673848771
Kurtosis550.2268722
Mean8.322776058
Median Absolute Deviation (MAD)1
Skewness19.46988775
Sum126398
Variance934.9297268
MonotonicityNot monotonic
2025-10-22T11:57:38.906399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23717
24.5%
13213
21.2%
31578
10.4%
301076
 
7.1%
1.3566
 
3.7%
2.9363
 
2.4%
4269
 
1.8%
2.1264
 
1.7%
2.8225
 
1.5%
2.3198
 
1.3%
Other values (303)3718
24.5%
ValueCountFrequency (%)
13213
21.2%
1.1118
 
0.8%
1.243
 
0.3%
1.3566
 
3.7%
1.4175
 
1.2%
ValueCountFrequency (%)
11242
 
< 0.1%
11001
 
< 0.1%
10001
 
< 0.1%
7305
< 0.1%
5002
 
< 0.1%

maximum_nights_avg_ntm
Real number (ℝ)

Distinct575
Distinct (%)3.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5785746.683
Minimum1
Maximum2147483647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:39.007216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q1180
median365
Q31125
95-th percentile1125
Maximum2147483647
Range2147483646
Interquartile range (IQR)945

Descriptive statistics

Standard deviation111194568.3
Coefficient of variation (CV)19.21870665
Kurtosis365.5399905
Mean5785746.683
Median Absolute Deviation (MAD)358
Skewness19.17006441
Sum8.786813488 × 1010
Variance1.236423201 × 1016
MonotonicityNot monotonic
2025-10-22T11:57:39.108231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11256669
43.9%
3653822
25.2%
30473
 
3.1%
14332
 
2.2%
7314
 
2.1%
90291
 
1.9%
60216
 
1.4%
28211
 
1.4%
180184
 
1.2%
10171
 
1.1%
Other values (565)2504
 
16.5%
ValueCountFrequency (%)
19
 
0.1%
224
 
0.2%
396
0.6%
481
0.5%
5136
0.9%
ValueCountFrequency (%)
21474836479
 
0.1%
21416001334
 
< 0.1%
214160013128
0.2%
103201
 
< 0.1%
18251
 
< 0.1%

calendar_updated
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing15187
Missing (%)> 99.9%
Memory size118.8 KiB
2025-10-22T11:57:39.197438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2022-07-31
ValueCountFrequency (%)
2022-07-311
100.0%
2025-10-22T11:57:39.328716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23
30.0%
02
20.0%
-2
20.0%
71
 
10.0%
31
 
10.0%
11
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
23
30.0%
02
20.0%
-2
20.0%
71
 
10.0%
31
 
10.0%
11
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
23
30.0%
02
20.0%
-2
20.0%
71
 
10.0%
31
 
10.0%
11
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
23
30.0%
02
20.0%
-2
20.0%
71
 
10.0%
31
 
10.0%
11
 
10.0%

has_availability
Text

Missing 

Distinct2
Distinct (%)< 0.1%
Missing1332
Missing (%)8.8%
Memory size118.8 KiB
2025-10-22T11:57:39.375205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length1
Mean length1.000649538
Min length1

Characters and Unicode

Total characters13865
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowt
2nd rowt
3rd rowt
4th rowt
5th rowt
ValueCountFrequency (%)
t13855
> 99.9%
2024-11-161
 
< 0.1%
2025-10-22T11:57:39.499668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t13855
99.9%
13
 
< 0.1%
22
 
< 0.1%
-2
 
< 0.1%
01
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)13865
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t13855
99.9%
13
 
< 0.1%
22
 
< 0.1%
-2
 
< 0.1%
01
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13865
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t13855
99.9%
13
 
< 0.1%
22
 
< 0.1%
-2
 
< 0.1%
01
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13865
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t13855
99.9%
13
 
< 0.1%
22
 
< 0.1%
-2
 
< 0.1%
01
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%

availability_30
Real number (ℝ)

Zeros 

Distinct32
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.48995325
Minimum0
Maximum30
Zeros5683
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:39.568469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q324
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)24

Descriptive statistics

Standard deviation11.73153989
Coefficient of variation (CV)0.9392781269
Kurtosis-1.573703986
Mean12.48995325
Median Absolute Deviation (MAD)12
Skewness0.2094803344
Sum189684.92
Variance137.6290283
MonotonicityNot monotonic
2025-10-22T11:57:39.649632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
05683
37.4%
301377
 
9.1%
29734
 
4.8%
28476
 
3.1%
23446
 
2.9%
27433
 
2.9%
12391
 
2.6%
20364
 
2.4%
16328
 
2.2%
24323
 
2.1%
Other values (22)4632
30.5%
ValueCountFrequency (%)
05683
37.4%
1145
 
1.0%
2131
 
0.9%
3112
 
0.7%
4130
 
0.9%
ValueCountFrequency (%)
301377
9.1%
29734
4.8%
28476
 
3.1%
27433
 
2.9%
26300
 
2.0%

availability_60
Real number (ℝ)

Zeros 

Distinct62
Distinct (%)0.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean28.74345954
Minimum0
Maximum60
Zeros4932
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:39.746873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median33
Q353
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)53

Descriptive statistics

Standard deviation24.15180428
Coefficient of variation (CV)0.8402539106
Kurtosis-1.683336558
Mean28.74345954
Median Absolute Deviation (MAD)25
Skewness-0.06875755012
Sum436526.92
Variance583.30965
MonotonicityNot monotonic
2025-10-22T11:57:39.846167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04932
32.5%
601284
 
8.5%
59679
 
4.5%
58405
 
2.7%
57378
 
2.5%
53358
 
2.4%
42305
 
2.0%
50289
 
1.9%
55275
 
1.8%
56263
 
1.7%
Other values (52)6019
39.6%
ValueCountFrequency (%)
04932
32.5%
1107
 
0.7%
281
 
0.5%
390
 
0.6%
448
 
0.3%
ValueCountFrequency (%)
601284
8.5%
59679
4.5%
58405
 
2.7%
57378
 
2.5%
56263
 
1.7%

availability_90
Real number (ℝ)

Zeros 

Distinct92
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean46.35469085
Minimum0
Maximum90
Zeros4479
Zeros (%)29.5%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:39.942956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median57
Q381
95-th percentile90
Maximum90
Range90
Interquartile range (IQR)81

Descriptive statistics

Standard deviation36.23531815
Coefficient of variation (CV)0.7816969003
Kurtosis-1.646683181
Mean46.35469085
Median Absolute Deviation (MAD)31
Skewness-0.215466765
Sum703988.69
Variance1312.998282
MonotonicityNot monotonic
2025-10-22T11:57:40.038201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04479
29.5%
901172
 
7.7%
89683
 
4.5%
88379
 
2.5%
87350
 
2.3%
83339
 
2.2%
72287
 
1.9%
80278
 
1.8%
85226
 
1.5%
86221
 
1.5%
Other values (82)6773
44.6%
ValueCountFrequency (%)
04479
29.5%
155
 
0.4%
241
 
0.3%
331
 
0.2%
431
 
0.2%
ValueCountFrequency (%)
901172
7.7%
89683
4.5%
88379
 
2.5%
87350
 
2.3%
86221
 
1.5%

availability_365
Real number (ℝ)

Zeros 

Distinct366
Distinct (%)2.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean174.4394548
Minimum0
Maximum365
Zeros3844
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:40.132922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median177
Q3323
95-th percentile363
Maximum365
Range365
Interquartile range (IQR)323

Descriptive statistics

Standard deviation140.9044203
Coefficient of variation (CV)0.8077554497
Kurtosis-1.612961215
Mean174.4394548
Median Absolute Deviation (MAD)154
Skewness-0.008243260627
Sum2649212
Variance19854.05565
MonotonicityNot monotonic
2025-10-22T11:57:40.232656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03844
 
25.3%
365488
 
3.2%
364255
 
1.7%
89156
 
1.0%
363140
 
0.9%
358137
 
0.9%
362136
 
0.9%
270119
 
0.8%
269119
 
0.8%
348112
 
0.7%
Other values (356)9681
63.7%
ValueCountFrequency (%)
03844
25.3%
121
 
0.1%
219
 
0.1%
325
 
0.2%
416
 
0.1%
ValueCountFrequency (%)
365488
3.2%
364255
1.7%
363140
 
0.9%
362136
 
0.9%
36186
 
0.6%
Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:40.322212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.999604925
Min length4

Characters and Unicode

Total characters151864
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2025-06-13
2nd row2025-06-13
3rd row2025-06-14
4th row2025-06-13
5th row2025-06-13
ValueCountFrequency (%)
2025-06-139702
63.9%
2025-06-145484
36.1%
4.921
 
< 0.1%
2025-10-22T11:57:40.473667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
230373
20.0%
030372
20.0%
-30372
20.0%
515186
10.0%
615186
10.0%
115186
10.0%
39702
 
6.4%
45485
 
3.6%
.1
 
< 0.1%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)151864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
230373
20.0%
030372
20.0%
-30372
20.0%
515186
10.0%
615186
10.0%
115186
10.0%
39702
 
6.4%
45485
 
3.6%
.1
 
< 0.1%
91
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)151864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
230373
20.0%
030372
20.0%
-30372
20.0%
515186
10.0%
615186
10.0%
115186
10.0%
39702
 
6.4%
45485
 
3.6%
.1
 
< 0.1%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)151864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
230373
20.0%
030372
20.0%
-30372
20.0%
515186
10.0%
615186
10.0%
115186
10.0%
39702
 
6.4%
45485
 
3.6%
.1
 
< 0.1%
91
 
< 0.1%

number_of_reviews
Real number (ℝ)

Zeros 

Distinct532
Distinct (%)3.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean44.17692237
Minimum0
Maximum1291
Zeros2911
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:40.556667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q346
95-th percentile202
Maximum1291
Range1291
Interquartile range (IQR)45

Descriptive statistics

Standard deviation91.25618059
Coefficient of variation (CV)2.065698009
Kurtosis33.91992933
Mean44.17692237
Median Absolute Deviation (MAD)10
Skewness4.769874487
Sum670914.92
Variance8327.690496
MonotonicityNot monotonic
2025-10-22T11:57:40.657927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02911
 
19.2%
11189
 
7.8%
2743
 
4.9%
3594
 
3.9%
4464
 
3.1%
5395
 
2.6%
6334
 
2.2%
7295
 
1.9%
8287
 
1.9%
9282
 
1.9%
Other values (522)7693
50.7%
ValueCountFrequency (%)
02911
19.2%
11189
7.8%
2743
 
4.9%
3594
 
3.9%
4464
 
3.1%
ValueCountFrequency (%)
12911
< 0.1%
12511
< 0.1%
12111
< 0.1%
11781
< 0.1%
11591
< 0.1%

number_of_reviews_ltm
Real number (ℝ)

Zeros 

Distinct130
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9.756481859
Minimum0
Maximum334
Zeros6126
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:40.759956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313
95-th percentile43
Maximum334
Range334
Interquartile range (IQR)13

Descriptive statistics

Standard deviation16.74309997
Coefficient of variation (CV)1.716100149
Kurtosis23.21984946
Mean9.756481859
Median Absolute Deviation (MAD)2
Skewness3.377424355
Sum148171.69
Variance280.3313967
MonotonicityNot monotonic
2025-10-22T11:57:40.859030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06126
40.3%
11146
 
7.5%
2703
 
4.6%
3594
 
3.9%
4471
 
3.1%
5407
 
2.7%
6318
 
2.1%
7317
 
2.1%
8272
 
1.8%
9268
 
1.8%
Other values (120)4565
30.1%
ValueCountFrequency (%)
06126
40.3%
11146
 
7.5%
2703
 
4.6%
3594
 
3.9%
4471
 
3.1%
ValueCountFrequency (%)
3341
< 0.1%
2451
< 0.1%
2041
< 0.1%
1971
< 0.1%
1891
< 0.1%

number_of_reviews_l30d
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.708415646
Minimum0
Maximum31
Zeros10623
Zeros (%)69.9%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:40.955814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum31
Range31
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.487891297
Coefficient of variation (CV)2.100308351
Kurtosis28.05583393
Mean0.708415646
Median Absolute Deviation (MAD)0
Skewness3.760262258
Sum10758
Variance2.213820512
MonotonicityNot monotonic
2025-10-22T11:57:41.182997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
010623
69.9%
11927
 
12.7%
21089
 
7.2%
3712
 
4.7%
4387
 
2.5%
5173
 
1.1%
6118
 
0.8%
757
 
0.4%
937
 
0.2%
829
 
0.2%
Other values (10)34
 
0.2%
ValueCountFrequency (%)
010623
69.9%
11927
 
12.7%
21089
 
7.2%
3712
 
4.7%
4387
 
2.5%
ValueCountFrequency (%)
311
< 0.1%
221
< 0.1%
201
< 0.1%
191
< 0.1%
171
< 0.1%
Distinct204
Distinct (%)1.3%
Missing1
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:41.428929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.267597287
Min length1

Characters and Unicode

Total characters34438
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row185
2nd row4
3rd row0
4th row149
5th row98
ValueCountFrequency (%)
04088
26.9%
202544
 
3.6%
201461
 
3.0%
195194
 
1.3%
200189
 
1.2%
179180
 
1.2%
199177
 
1.2%
184168
 
1.1%
89161
 
1.1%
185155
 
1.0%
Other values (194)8870
58.4%
2025-10-22T11:57:41.778429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19164
26.6%
06649
19.3%
23206
 
9.3%
82852
 
8.3%
92832
 
8.2%
72579
 
7.5%
61976
 
5.7%
51832
 
5.3%
41697
 
4.9%
31650
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)34438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19164
26.6%
06649
19.3%
23206
 
9.3%
82852
 
8.3%
92832
 
8.2%
72579
 
7.5%
61976
 
5.7%
51832
 
5.3%
41697
 
4.9%
31650
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)34438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19164
26.6%
06649
19.3%
23206
 
9.3%
82852
 
8.3%
92832
 
8.2%
72579
 
7.5%
61976
 
5.7%
51832
 
5.3%
41697
 
4.9%
31650
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)34438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19164
26.6%
06649
19.3%
23206
 
9.3%
82852
 
8.3%
92832
 
8.2%
72579
 
7.5%
61976
 
5.7%
51832
 
5.3%
41697
 
4.9%
31650
 
4.8%

number_of_reviews_ly
Real number (ℝ)

Zeros 

Distinct130
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9.278198459
Minimum0
Maximum217
Zeros7095
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:41.871024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q312
95-th percentile45
Maximum217
Range217
Interquartile range (IQR)12

Descriptive statistics

Standard deviation16.8831046
Coefficient of variation (CV)1.819653317
Kurtosis12.42549376
Mean9.278198459
Median Absolute Deviation (MAD)1
Skewness2.967885914
Sum140908
Variance285.0392211
MonotonicityNot monotonic
2025-10-22T11:57:41.970764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07095
46.7%
1956
 
6.3%
2647
 
4.3%
3516
 
3.4%
4396
 
2.6%
5368
 
2.4%
6289
 
1.9%
8236
 
1.6%
7235
 
1.5%
9224
 
1.5%
Other values (120)4225
27.8%
ValueCountFrequency (%)
07095
46.7%
1956
 
6.3%
2647
 
4.3%
3516
 
3.4%
4396
 
2.6%
ValueCountFrequency (%)
2171
< 0.1%
1592
< 0.1%
1572
< 0.1%
1551
< 0.1%
1532
< 0.1%

estimated_occupancy_l365d
Real number (ℝ)

Zeros 

Distinct81
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean62.12820175
Minimum0
Maximum255
Zeros6126
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:42.071737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18
Q3108
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)108

Descriptive statistics

Standard deviation82.42509276
Coefficient of variation (CV)1.326693682
Kurtosis0.203098074
Mean62.12820175
Median Absolute Deviation (MAD)18
Skewness1.229356727
Sum943541
Variance6793.895917
MonotonicityNot monotonic
2025-10-22T11:57:42.173256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06126
40.3%
2551080
 
7.1%
6718
 
4.7%
12432
 
2.8%
60428
 
2.8%
18412
 
2.7%
24347
 
2.3%
30330
 
2.2%
120289
 
1.9%
42282
 
1.9%
Other values (71)4743
31.2%
ValueCountFrequency (%)
06126
40.3%
11
 
< 0.1%
6718
 
4.7%
831
 
0.2%
1024
 
0.2%
ValueCountFrequency (%)
2551080
7.1%
25247
 
0.3%
2485
 
< 0.1%
24635
 
0.2%
240137
 
0.9%

estimated_revenue_l365d
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct3419
Distinct (%)31.9%
Missing4480
Missing (%)29.5%
Infinite0
Infinite (%)0.0%
Mean15185.70405
Minimum0
Maximum3900000
Zeros2459
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:42.276280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1701.5
median7073
Q319656
95-th percentile49754.25
Maximum3900000
Range3900000
Interquartile range (IQR)18954.5

Descriptive statistics

Standard deviation52453.02355
Coefficient of variation (CV)3.454105478
Kurtosis3277.668495
Mean15185.70405
Median Absolute Deviation (MAD)7073
Skewness49.73683497
Sum162608519
Variance2751319680
MonotonicityNot monotonic
2025-10-22T11:57:42.383699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02459
 
16.2%
540028
 
0.2%
2040021
 
0.1%
180019
 
0.1%
720018
 
0.1%
102018
 
0.1%
600018
 
0.1%
432017
 
0.1%
1080017
 
0.1%
2448017
 
0.1%
Other values (3409)8076
53.2%
(Missing)4480
29.5%
ValueCountFrequency (%)
02459
16.2%
1442
 
< 0.1%
1501
 
< 0.1%
1801
 
< 0.1%
1921
 
< 0.1%
ValueCountFrequency (%)
39000001
< 0.1%
24000001
< 0.1%
15000001
< 0.1%
9000001
< 0.1%
4995001
< 0.1%

first_review
Text

Missing 

Distinct2998
Distinct (%)24.4%
Missing2912
Missing (%)19.2%
Memory size118.8 KiB
2025-10-22T11:57:42.564534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.999266862
Min length1

Characters and Unicode

Total characters122751
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1032 ?
Unique (%)8.4%

Sample

1st row2009-03-08
2nd row2010-04-10
3rd row2009-12-14
4th row2011-09-06
5th row2010-02-19
ValueCountFrequency (%)
2024-10-2097
 
0.8%
2024-10-2146
 
0.4%
2025-03-0945
 
0.4%
2024-10-0744
 
0.4%
2023-10-2244
 
0.4%
2024-03-0344
 
0.4%
2025-02-1643
 
0.4%
2024-10-1443
 
0.4%
2023-10-0839
 
0.3%
2025-03-3038
 
0.3%
Other values (2988)11793
96.1%
2025-10-22T11:57:42.803301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
229088
23.7%
028421
23.2%
-24550
20.0%
115138
12.3%
36259
 
5.1%
44750
 
3.9%
53929
 
3.2%
92829
 
2.3%
72738
 
2.2%
82618
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)122751
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
229088
23.7%
028421
23.2%
-24550
20.0%
115138
12.3%
36259
 
5.1%
44750
 
3.9%
53929
 
3.2%
92829
 
2.3%
72738
 
2.2%
82618
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)122751
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
229088
23.7%
028421
23.2%
-24550
20.0%
115138
12.3%
36259
 
5.1%
44750
 
3.9%
53929
 
3.2%
92829
 
2.3%
72738
 
2.2%
82618
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)122751
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
229088
23.7%
028421
23.2%
-24550
20.0%
115138
12.3%
36259
 
5.1%
44750
 
3.9%
53929
 
3.2%
92829
 
2.3%
72738
 
2.2%
82618
 
2.1%

last_review
Text

Missing 

Distinct1640
Distinct (%)13.4%
Missing2912
Missing (%)19.2%
Memory size118.8 KiB
2025-10-22T11:57:42.979479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.999511241
Min length4

Characters and Unicode

Total characters122754
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique737 ?
Unique (%)6.0%

Sample

1st row2025-04-27
2nd row2025-06-08
3rd row2025-03-12
4th row2025-05-31
5th row2025-05-05
ValueCountFrequency (%)
2025-05-26635
 
5.2%
2025-06-08510
 
4.2%
2025-06-01400
 
3.3%
2025-05-25345
 
2.8%
2025-05-11343
 
2.8%
2025-05-18239
 
1.9%
2025-06-02215
 
1.8%
2025-06-09203
 
1.7%
2025-04-27198
 
1.6%
2025-05-31155
 
1.3%
Other values (1630)9033
73.6%
2025-10-22T11:57:43.235955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
229472
24.0%
029046
23.7%
-24550
20.0%
512710
10.4%
19610
 
7.8%
44237
 
3.5%
63961
 
3.2%
33909
 
3.2%
81999
 
1.6%
71672
 
1.4%
Other values (2)1588
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)122754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
229472
24.0%
029046
23.7%
-24550
20.0%
512710
10.4%
19610
 
7.8%
44237
 
3.5%
63961
 
3.2%
33909
 
3.2%
81999
 
1.6%
71672
 
1.4%
Other values (2)1588
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)122754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
229472
24.0%
029046
23.7%
-24550
20.0%
512710
10.4%
19610
 
7.8%
44237
 
3.5%
63961
 
3.2%
33909
 
3.2%
81999
 
1.6%
71672
 
1.4%
Other values (2)1588
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)122754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
229472
24.0%
029046
23.7%
-24550
20.0%
512710
10.4%
19610
 
7.8%
44237
 
3.5%
63961
 
3.2%
33909
 
3.2%
81999
 
1.6%
71672
 
1.4%
Other values (2)1588
 
1.3%

review_scores_rating
Real number (ℝ)

Missing 

Distinct124
Distinct (%)1.0%
Missing2912
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean4.832033236
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:43.329240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.33
Q14.8
median4.94
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.3463027001
Coefficient of variation (CV)0.07166811221
Kurtosis55.84738439
Mean4.832033236
Median Absolute Deviation (MAD)0.06
Skewness-6.224300732
Sum59318.04
Variance0.1199255601
MonotonicityNot monotonic
2025-10-22T11:57:43.435611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54260
28.0%
4.96372
 
2.4%
4.94364
 
2.4%
4.95359
 
2.4%
4.92326
 
2.1%
4.97324
 
2.1%
4.93321
 
2.1%
4.98314
 
2.1%
4.88309
 
2.0%
4.89276
 
1.8%
Other values (114)5051
33.3%
(Missing)2912
19.2%
ValueCountFrequency (%)
02
 
< 0.1%
133
0.2%
1.52
 
< 0.1%
212
 
0.1%
2.332
 
< 0.1%
ValueCountFrequency (%)
54260
28.0%
4.99174
 
1.1%
4.98314
 
2.1%
4.97324
 
2.1%
4.96372
 
2.4%

review_scores_accuracy
Real number (ℝ)

Missing  Skewed 

Distinct121
Distinct (%)1.0%
Missing2912
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean4.868211144
Minimum1
Maximum209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:43.537815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.45
Q14.84
median4.95
Q35
95-th percentile5
Maximum209
Range208
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation1.873357162
Coefficient of variation (CV)0.3848142791
Kurtosis11487.88577
Mean4.868211144
Median Absolute Deviation (MAD)0.05
Skewness105.3796147
Sum59762.16
Variance3.509467056
MonotonicityNot monotonic
2025-10-22T11:57:43.637149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54516
29.7%
4.94430
 
2.8%
4.96414
 
2.7%
4.98410
 
2.7%
4.97378
 
2.5%
4.93375
 
2.5%
4.95369
 
2.4%
4.92339
 
2.2%
4.88311
 
2.0%
4.91300
 
2.0%
Other values (111)4434
29.2%
(Missing)2912
19.2%
ValueCountFrequency (%)
134
0.2%
218
0.1%
2.331
 
< 0.1%
2.53
 
< 0.1%
2.671
 
< 0.1%
ValueCountFrequency (%)
2091
 
< 0.1%
54516
29.7%
4.99176
 
1.2%
4.98410
 
2.7%
4.97378
 
2.5%

review_scores_cleanliness
Real number (ℝ)

Missing 

Distinct138
Distinct (%)1.1%
Missing2912
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean4.807117954
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:43.736278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.25
Q14.77
median4.92
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.3655763447
Coefficient of variation (CV)0.07604896493
Kurtosis41.44418742
Mean4.807117954
Median Absolute Deviation (MAD)0.08
Skewness-5.306848966
Sum59012.18
Variance0.1336460638
MonotonicityNot monotonic
2025-10-22T11:57:43.841833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53996
26.3%
4.95335
 
2.2%
4.94325
 
2.1%
4.96318
 
2.1%
4.93305
 
2.0%
4.97300
 
2.0%
4.91291
 
1.9%
4.92282
 
1.9%
4.86271
 
1.8%
4.88269
 
1.8%
Other values (128)5584
36.8%
(Missing)2912
19.2%
ValueCountFrequency (%)
01
 
< 0.1%
132
0.2%
215
0.1%
2.251
 
< 0.1%
2.331
 
< 0.1%
ValueCountFrequency (%)
53996
26.3%
4.99141
 
0.9%
4.98260
 
1.7%
4.97300
 
2.0%
4.96318
 
2.1%

review_scores_checkin
Real number (ℝ)

Missing  Skewed 

Distinct108
Distinct (%)0.9%
Missing2913
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean4.898041548
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:43.939917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.54
Q14.9
median4.98
Q35
95-th percentile5
Maximum57
Range56
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.5558015466
Coefficient of variation (CV)0.1134742409
Kurtosis6298.71476
Mean4.898041548
Median Absolute Deviation (MAD)0.02
Skewness65.92099942
Sum60123.46
Variance0.3089153592
MonotonicityNot monotonic
2025-10-22T11:57:44.038495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55707
37.6%
4.98539
 
3.5%
4.97494
 
3.3%
4.96428
 
2.8%
4.99404
 
2.7%
4.95391
 
2.6%
4.94370
 
2.4%
4.93308
 
2.0%
4.92281
 
1.9%
4.88215
 
1.4%
Other values (98)3138
20.7%
(Missing)2913
19.2%
ValueCountFrequency (%)
131
0.2%
25
 
< 0.1%
2.331
 
< 0.1%
2.52
 
< 0.1%
340
0.3%
ValueCountFrequency (%)
571
 
< 0.1%
55707
37.6%
4.99404
 
2.7%
4.98539
 
3.5%
4.97494
 
3.3%

review_scores_communication
Real number (ℝ)

Missing 

Distinct111
Distinct (%)0.9%
Missing2913
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean4.897549491
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:44.137657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.59
Q14.91
median4.99
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.3016117003
Coefficient of variation (CV)0.06158420674
Kurtosis86.13328005
Mean4.897549491
Median Absolute Deviation (MAD)0.01
Skewness-8.100073155
Sum60117.42
Variance0.09096961778
MonotonicityNot monotonic
2025-10-22T11:57:44.244305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55901
38.9%
4.98498
 
3.3%
4.97484
 
3.2%
4.96429
 
2.8%
4.99395
 
2.6%
4.94375
 
2.5%
4.95363
 
2.4%
4.93297
 
2.0%
4.92270
 
1.8%
4.89220
 
1.4%
Other values (101)3043
20.0%
(Missing)2913
19.2%
ValueCountFrequency (%)
132
0.2%
1.751
 
< 0.1%
29
 
0.1%
2.52
 
< 0.1%
2.673
 
< 0.1%
ValueCountFrequency (%)
55901
38.9%
4.99395
 
2.6%
4.98498
 
3.3%
4.97484
 
3.2%
4.96429
 
2.8%

review_scores_location
Real number (ℝ)

Missing 

Distinct134
Distinct (%)1.1%
Missing2915
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean4.820195551
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:44.497975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.33
Q14.78
median4.91
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.3237256294
Coefficient of variation (CV)0.06716026891
Kurtosis53.4784469
Mean4.820195551
Median Absolute Deviation (MAD)0.09
Skewness-5.843649681
Sum59158.26
Variance0.1047982831
MonotonicityNot monotonic
2025-10-22T11:57:44.603537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53773
24.8%
4.92352
 
2.3%
4.95349
 
2.3%
4.94335
 
2.2%
4.88322
 
2.1%
4.93319
 
2.1%
4.86319
 
2.1%
4.89311
 
2.0%
4.96303
 
2.0%
4.91288
 
1.9%
Other values (124)5602
36.9%
(Missing)2915
19.2%
ValueCountFrequency (%)
02
 
< 0.1%
121
0.1%
1.52
 
< 0.1%
213
0.1%
2.52
 
< 0.1%
ValueCountFrequency (%)
53773
24.8%
4.9986
 
0.6%
4.98207
 
1.4%
4.97266
 
1.8%
4.96303
 
2.0%

review_scores_value
Real number (ℝ)

Missing 

Distinct132
Distinct (%)1.1%
Missing2915
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean4.772880306
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:44.714718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.25
Q14.73
median4.86
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.3707396938
Coefficient of variation (CV)0.07767630236
Kurtosis41.85888934
Mean4.772880306
Median Absolute Deviation (MAD)0.13
Skewness-5.340690776
Sum58577.56
Variance0.1374479206
MonotonicityNot monotonic
2025-10-22T11:57:44.820870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53083
20.3%
4.88401
 
2.6%
4.86354
 
2.3%
4.83348
 
2.3%
4.8336
 
2.2%
4.89335
 
2.2%
4.91318
 
2.1%
4.67293
 
1.9%
4.75292
 
1.9%
4.9288
 
1.9%
Other values (122)6225
41.0%
(Missing)2915
19.2%
ValueCountFrequency (%)
01
 
< 0.1%
136
0.2%
1.751
 
< 0.1%
218
0.1%
2.21
 
< 0.1%
ValueCountFrequency (%)
53083
20.3%
4.9914
 
0.1%
4.9872
 
0.5%
4.97106
 
0.7%
4.96202
 
1.3%

license
Unsupported

Missing  Rejected  Unsupported 

Missing15188
Missing (%)100.0%
Memory size118.8 KiB
Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size118.8 KiB
2025-10-22T11:57:44.876231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15186
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd rowf
3rd rowf
4th rowt
5th rowf
ValueCountFrequency (%)
f9914
65.3%
t5272
34.7%
2025-10-22T11:57:44.988277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f9914
65.3%
t5272
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)15186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f9914
65.3%
t5272
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f9914
65.3%
t5272
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f9914
65.3%
t5272
34.7%
Distinct48
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9.716054261
Minimum1
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:45.062094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile57
Maximum116
Range115
Interquartile range (IQR)5

Descriptive statistics

Standard deviation20.14537162
Coefficient of variation (CV)2.073410778
Kurtosis11.00615404
Mean9.716054261
Median Absolute Deviation (MAD)1
Skewness3.281267623
Sum147548
Variance405.8359978
MonotonicityNot monotonic
2025-10-22T11:57:45.161155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
17030
46.3%
21954
 
12.9%
31128
 
7.4%
4616
 
4.1%
5395
 
2.6%
6288
 
1.9%
8248
 
1.6%
7203
 
1.3%
14168
 
1.1%
19152
 
1.0%
Other values (38)3004
19.8%
ValueCountFrequency (%)
17030
46.3%
21954
 
12.9%
31128
 
7.4%
4616
 
4.1%
5395
 
2.6%
ValueCountFrequency (%)
116116
0.8%
9797
0.6%
9494
0.6%
9090
0.6%
8383
0.5%
Distinct44
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.181812195
Minimum0
Maximum116
Zeros2181
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:45.256042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile34
Maximum116
Range116
Interquartile range (IQR)3

Descriptive statistics

Standard deviation16.73114017
Coefficient of variation (CV)2.329654371
Kurtosis19.1707073
Mean7.181812195
Median Absolute Deviation (MAD)1
Skewness4.115968279
Sum109063
Variance279.9310515
MonotonicityNot monotonic
2025-10-22T11:57:45.350950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
16522
42.9%
02181
 
14.4%
21603
 
10.6%
3773
 
5.1%
4404
 
2.7%
5311
 
2.0%
6231
 
1.5%
8219
 
1.4%
9191
 
1.3%
14174
 
1.1%
Other values (34)2577
 
17.0%
ValueCountFrequency (%)
02181
 
14.4%
16522
42.9%
21603
 
10.6%
3773
 
5.1%
4404
 
2.7%
ValueCountFrequency (%)
116116
0.8%
9697
0.6%
7373
0.5%
6565
0.4%
6060
0.4%
Distinct21
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.271434216
Minimum0
Maximum57
Zeros11962
Zeros (%)78.8%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:45.428363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum57
Range57
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.491665479
Coefficient of variation (CV)4.319268281
Kurtosis52.48435564
Mean1.271434216
Median Absolute Deviation (MAD)0
Skewness6.777723917
Sum19308
Variance30.15838974
MonotonicityNot monotonic
2025-10-22T11:57:45.502399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
011962
78.8%
11533
 
10.1%
2457
 
3.0%
3328
 
2.2%
4154
 
1.0%
12114
 
0.8%
3494
 
0.6%
580
 
0.5%
5757
 
0.4%
656
 
0.4%
Other values (11)351
 
2.3%
ValueCountFrequency (%)
011962
78.8%
11533
 
10.1%
2457
 
3.0%
3328
 
2.2%
4154
 
1.0%
ValueCountFrequency (%)
5757
0.4%
3636
 
0.2%
3494
0.6%
3030
 
0.2%
2929
 
0.2%
Distinct4
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.3760700645
Minimum0
Maximum60
Zeros15039
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:45.572294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum60
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.706459204
Coefficient of variation (CV)12.51484669
Kurtosis156.510178
Mean0.3760700645
Median Absolute Deviation (MAD)0
Skewness12.58732907
Sum5711
Variance22.15075824
MonotonicityNot monotonic
2025-10-22T11:57:45.649238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
015039
99.0%
6094
 
0.6%
135
 
0.2%
218
 
0.1%
(Missing)2
 
< 0.1%
ValueCountFrequency (%)
015039
99.0%
135
 
0.2%
218
 
0.1%
6094
 
0.6%
ValueCountFrequency (%)
6094
 
0.6%
218
 
0.1%
135
 
0.2%
015039
99.0%

reviews_per_month
Real number (ℝ)

Zeros 

Distinct784
Distinct (%)5.2%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.176864217
Minimum0
Maximum23.08
Zeros2911
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:45.747450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.04
median0.49
Q31.79
95-th percentile4.36
Maximum23.08
Range23.08
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation1.616517452
Coefficient of variation (CV)1.373580255
Kurtosis12.39389954
Mean1.176864217
Median Absolute Deviation (MAD)0.49
Skewness2.548339594
Sum17871.86
Variance2.613128671
MonotonicityNot monotonic
2025-10-22T11:57:45.848603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02911
 
19.2%
0.01315
 
2.1%
0.03226
 
1.5%
0.02220
 
1.4%
0.04200
 
1.3%
0.05182
 
1.2%
0.06178
 
1.2%
0.13172
 
1.1%
0.07159
 
1.0%
1150
 
1.0%
Other values (774)10473
69.0%
ValueCountFrequency (%)
02911
19.2%
0.01315
 
2.1%
0.02220
 
1.4%
0.03226
 
1.5%
0.04200
 
1.3%
ValueCountFrequency (%)
23.081
< 0.1%
21.431
< 0.1%
20.491
< 0.1%
20.381
< 0.1%
15.841
< 0.1%

reviews_per_month_imputed
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros15186
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:45.919331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2025-10-22T11:57:45.991506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
015186
> 99.9%
(Missing)2
 
< 0.1%
ValueCountFrequency (%)
015186
> 99.9%
ValueCountFrequency (%)
015186
> 99.9%

days_since_last_review
Real number (ℝ)

Distinct1639
Distinct (%)10.8%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean402.4838667
Minimum0
Maximum5163
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:46.078355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q121
median132
Q3236
95-th percentile2434.75
Maximum5163
Range5163
Interquartile range (IQR)215

Descriptive statistics

Standard deviation769.6968661
Coefficient of variation (CV)1.912367003
Kurtosis6.439631738
Mean402.4838667
Median Absolute Deviation (MAD)110
Skewness2.655973998
Sum6112120
Variance592433.2656
MonotonicityNot monotonic
2025-10-22T11:57:46.179817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1622926
 
19.3%
18635
 
4.2%
5510
 
3.4%
12400
 
2.6%
19345
 
2.3%
33343
 
2.3%
26239
 
1.6%
11215
 
1.4%
4203
 
1.3%
47198
 
1.3%
Other values (1629)9172
60.4%
ValueCountFrequency (%)
06
 
< 0.1%
1110
0.7%
270
 
0.5%
392
0.6%
4203
1.3%
ValueCountFrequency (%)
51631
< 0.1%
48351
< 0.1%
45881
< 0.1%
44761
< 0.1%
44751
< 0.1%

days_since_last_review_imputed
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros15186
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:46.246611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2025-10-22T11:57:46.305918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
015186
> 99.9%
(Missing)2
 
< 0.1%
ValueCountFrequency (%)
015186
> 99.9%
ValueCountFrequency (%)
015186
> 99.9%

num_amenities
Real number (ℝ)

Distinct107
Distinct (%)0.7%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.19432372
Minimum1
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.8 KiB
2025-10-22T11:57:46.386887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q124
median41
Q355
95-th percentile70
Maximum116
Range115
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.22587015
Coefficient of variation (CV)0.4783230161
Kurtosis-0.7243572261
Mean40.19432372
Median Absolute Deviation (MAD)15
Skewness0.1161705182
Sum610391
Variance369.634083
MonotonicityNot monotonic
2025-10-22T11:57:46.482702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49311
 
2.0%
14310
 
2.0%
15304
 
2.0%
53298
 
2.0%
16294
 
1.9%
43292
 
1.9%
32287
 
1.9%
50286
 
1.9%
62285
 
1.9%
40284
 
1.9%
Other values (97)12235
80.6%
ValueCountFrequency (%)
144
0.3%
28
 
0.1%
33
 
< 0.1%
411
 
0.1%
520
0.1%
ValueCountFrequency (%)
1161
< 0.1%
1101
< 0.1%
1091
< 0.1%
1081
< 0.1%
1052
< 0.1%